Overview

Brought to you by YData

Dataset statistics

Number of variables43
Number of observations22788
Missing cells139329
Missing cells (%)14.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 MiB
Average record size in memory344.0 B

Variable types

Numeric19
Categorical14
Text8
DateTime2

Alerts

AMORTIZACION is highly overall correlated with CODIGO_TIPO_AMORTIZACION and 7 other fieldsHigh correlation
AÑO_REGISTRO is highly overall correlated with AÑO_VENCIMIENTO and 8 other fieldsHigh correlation
AÑO_VENCIMIENTO is highly overall correlated with AÑO_REGISTRO and 9 other fieldsHigh correlation
CANTIDAD_CUOTAS_PAGADAS is highly overall correlated with AÑO_REGISTRO and 9 other fieldsHigh correlation
CODIGO_CLIENTE is highly overall correlated with DELEGACION_MUNICIPIO and 2 other fieldsHigh correlation
CODIGO_MONEDA is highly overall correlated with AÑO_REGISTRO and 7 other fieldsHigh correlation
CODIGO_TIPO_AMORTIZACION is highly overall correlated with AMORTIZACION and 7 other fieldsHigh correlation
CODIGO_TIPO_IDENTIFICACION is highly overall correlated with DELEGACION_MUNICIPIO and 4 other fieldsHigh correlation
DELEGACION_MUNICIPIO is highly overall correlated with AMORTIZACION and 30 other fieldsHigh correlation
DESC_LINEA_FINANCIERA is highly overall correlated with AMORTIZACION and 5 other fieldsHigh correlation
DIA_VENCIMIENTO is highly overall correlated with DELEGACION_MUNICIPIOHigh correlation
ENTIDAD_FEDERATIVA is highly overall correlated with AMORTIZACION and 26 other fieldsHigh correlation
MES_REGISTRO is highly overall correlated with DELEGACION_MUNICIPIO and 3 other fieldsHigh correlation
MES_VENCIMIENTO is highly overall correlated with CANTIDAD_CUOTAS_PAGADAS and 3 other fieldsHigh correlation
MONTO_APROBADO is highly overall correlated with AÑO_VENCIMIENTO and 7 other fieldsHigh correlation
MONTO_DISPONIBLE is highly overall correlated with DELEGACION_MUNICIPIO and 2 other fieldsHigh correlation
MONTO_INICIAL is highly overall correlated with AÑO_VENCIMIENTO and 7 other fieldsHigh correlation
NACIONALIDAD is highly overall correlated with DELEGACION_MUNICIPIO and 2 other fieldsHigh correlation
NO_EMPLEADOS is highly overall correlated with CODIGO_CLIENTE and 4 other fieldsHigh correlation
NUMERO_CONTRATO is highly overall correlated with AÑO_REGISTRO and 9 other fieldsHigh correlation
NUMERO_CUOTAS is highly overall correlated with AÑO_REGISTRO and 11 other fieldsHigh correlation
NUMERO_PRESTAMO is highly overall correlated with AÑO_REGISTRO and 9 other fieldsHigh correlation
PAIS is highly overall correlated with DELEGACION_MUNICIPIO and 2 other fieldsHigh correlation
PLAZO is highly overall correlated with AMORTIZACION and 13 other fieldsHigh correlation
PRODUCTO is highly overall correlated with AMORTIZACION and 9 other fieldsHigh correlation
SEXO is highly overall correlated with CODIGO_TIPO_IDENTIFICACION and 8 other fieldsHigh correlation
TASA_TOTAL is highly overall correlated with CODIGO_MONEDA and 2 other fieldsHigh correlation
TIPO_SECTOR is highly overall correlated with DELEGACION_MUNICIPIO and 2 other fieldsHigh correlation
TIPO_SUBSECTOR is highly overall correlated with AMORTIZACION and 6 other fieldsHigh correlation
TIPO_TASA is highly overall correlated with AMORTIZACION and 6 other fieldsHigh correlation
VALOR_CUOTA is highly overall correlated with DELEGACION_MUNICIPIO and 4 other fieldsHigh correlation
VENTAS_NETAS is highly overall correlated with DELEGACION_MUNICIPIO and 3 other fieldsHigh correlation
PAIS is highly imbalanced (99.9%) Imbalance
NACIONALIDAD is highly imbalanced (99.9%) Imbalance
PRODUCTO is highly imbalanced (64.0%) Imbalance
CODIGO_MONEDA is highly imbalanced (76.4%) Imbalance
TIPO_TASA is highly imbalanced (50.0%) Imbalance
NOMBRES has 15849 (69.5%) missing values Missing
PRIMER_APELLIDO has 15849 (69.5%) missing values Missing
SEGUNDO_APELLIDO has 15849 (69.5%) missing values Missing
SEXO has 15849 (69.5%) missing values Missing
FECHA_DE_NACIMIENTO has 15849 (69.5%) missing values Missing
RAZON_SOCIAL has 6939 (30.5%) missing values Missing
NOMBRE_COMERCIAL has 6939 (30.5%) missing values Missing
DELEGACION_MUNICIPIO has 22033 (96.7%) missing values Missing
ENTIDAD_FEDERATIVA has 22033 (96.7%) missing values Missing
VALOR_CUOTA has 234 (1.0%) missing values Missing
CANTIDAD_CUOTAS_PAGADAS has 257 (1.1%) missing values Missing
NO_EMPLEADOS is highly skewed (γ1 = 31.64203232) Skewed
VENTAS_NETAS is highly skewed (γ1 = 58.25274773) Skewed
AÑO_VENCIMIENTO is highly skewed (γ1 = 39.37348963) Skewed
MONTO_APROBADO is highly skewed (γ1 = 76.07952098) Skewed
MONTO_DISPONIBLE is highly skewed (γ1 = 66.25622934) Skewed
MONTO_INICIAL is highly skewed (γ1 = 97.26062948) Skewed
VALOR_CUOTA is highly skewed (γ1 = 139.5894675) Skewed
MONTO_DISPONIBLE has 21570 (94.7%) zeros Zeros
VALOR_CUOTA has 299 (1.3%) zeros Zeros
CANTIDAD_CUOTAS_PAGADAS has 16848 (73.9%) zeros Zeros

Reproduction

Analysis started2025-02-11 16:41:19.807582
Analysis finished2025-02-11 16:42:12.537498
Duration52.73 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

CODIGO_CLIENTE
Real number (ℝ)

High correlation 

Distinct5000
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21271267
Minimum63041
Maximum28958014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:12.637916image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum63041
5-th percentile3912887
Q111166973
median28278053
Q328778209
95-th percentile28920410
Maximum28958014
Range28894973
Interquartile range (IQR)17611236

Descriptive statistics

Standard deviation10116601
Coefficient of variation (CV)0.47559934
Kurtosis-1.0423473
Mean21271267
Median Absolute Deviation (MAD)614362
Skewness-0.84304581
Sum4.8472964 × 1011
Variance1.0234561 × 1014
MonotonicityIncreasing
2025-02-11T10:42:12.781834image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21678811 781
 
3.4%
11873011 742
 
3.3%
5313244 698
 
3.1%
7663354 531
 
2.3%
28175306 490
 
2.2%
28803403 445
 
2.0%
28653245 374
 
1.6%
6005914 265
 
1.2%
14841326 235
 
1.0%
28183253 203
 
0.9%
Other values (4990) 18024
79.1%
ValueCountFrequency (%)
63041 1
 
< 0.1%
99003 3
 
< 0.1%
123173 2
 
< 0.1%
128196 66
 
0.3%
137337 2
 
< 0.1%
203738 1
 
< 0.1%
335024 183
0.8%
367786 1
 
< 0.1%
397726 2
 
< 0.1%
419934 1
 
< 0.1%
ValueCountFrequency (%)
28958014 3
< 0.1%
28951682 1
 
< 0.1%
28951280 1
 
< 0.1%
28951011 1
 
< 0.1%
28950852 1
 
< 0.1%
28950790 1
 
< 0.1%
28950781 1
 
< 0.1%
28950763 1
 
< 0.1%
28950674 1
 
< 0.1%
28950665 1
 
< 0.1%

CODIGO_TIPO_IDENTIFICACION
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
7
15849 
5
6939 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22788
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
7 15849
69.5%
5 6939
30.5%

Length

2025-02-11T10:42:12.935042image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-11T10:42:13.041742image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
7 15849
69.5%
5 6939
30.5%

Most occurring characters

ValueCountFrequency (%)
7 15849
69.5%
5 6939
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 15849
69.5%
5 6939
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 15849
69.5%
5 6939
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 15849
69.5%
5 6939
30.5%

NOMBRES
Text

Missing 

Distinct1799
Distinct (%)25.9%
Missing15849
Missing (%)69.5%
Memory size178.2 KiB
2025-02-11T10:42:13.479139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length25
Median length21
Mean length10.020608
Min length3

Characters and Unicode

Total characters69533
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1139 ?
Unique (%)16.4%

Sample

1st rowISIDRO ERNESTO
2nd rowJESUS
3rd rowJESUS
4th rowJESUS
5th rowJESUS
ValueCountFrequency (%)
jose 380
 
3.5%
maria 361
 
3.3%
manuel 269
 
2.5%
martha 244
 
2.2%
martin 234
 
2.1%
ofelia 207
 
1.9%
jesus 202
 
1.8%
juan 193
 
1.8%
victor 170
 
1.6%
antonio 170
 
1.6%
Other values (975) 8531
77.8%
2025-02-11T10:42:14.057477image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 11167
16.1%
E 6480
 
9.3%
R 6190
 
8.9%
I 5470
 
7.9%
O 5319
 
7.6%
N 4571
 
6.6%
L 4379
 
6.3%
4043
 
5.8%
S 2662
 
3.8%
M 2597
 
3.7%
Other values (18) 16655
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69533
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 11167
16.1%
E 6480
 
9.3%
R 6190
 
8.9%
I 5470
 
7.9%
O 5319
 
7.6%
N 4571
 
6.6%
L 4379
 
6.3%
4043
 
5.8%
S 2662
 
3.8%
M 2597
 
3.7%
Other values (18) 16655
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69533
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 11167
16.1%
E 6480
 
9.3%
R 6190
 
8.9%
I 5470
 
7.9%
O 5319
 
7.6%
N 4571
 
6.6%
L 4379
 
6.3%
4043
 
5.8%
S 2662
 
3.8%
M 2597
 
3.7%
Other values (18) 16655
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69533
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 11167
16.1%
E 6480
 
9.3%
R 6190
 
8.9%
I 5470
 
7.9%
O 5319
 
7.6%
N 4571
 
6.6%
L 4379
 
6.3%
4043
 
5.8%
S 2662
 
3.8%
M 2597
 
3.7%
Other values (18) 16655
24.0%

PRIMER_APELLIDO
Text

Missing 

Distinct989
Distinct (%)14.3%
Missing15849
Missing (%)69.5%
Memory size178.2 KiB
2025-02-11T10:42:14.475484image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length19
Median length12
Mean length6.5631935
Min length2

Characters and Unicode

Total characters45542
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique459 ?
Unique (%)6.6%

Sample

1st rowCONDE
2nd rowGONZALEZ
3rd rowGONZALEZ
4th rowGONZALEZ
5th rowGONZALEZ
ValueCountFrequency (%)
felix 323
 
4.6%
cortes 214
 
3.0%
rodriguez 211
 
3.0%
gonzalez 199
 
2.8%
lopez 183
 
2.6%
silva 157
 
2.2%
garcia 133
 
1.9%
martinez 133
 
1.9%
hernandez 110
 
1.6%
robles 105
 
1.5%
Other values (968) 5304
75.0%
2025-02-11T10:42:15.049232image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 6174
13.6%
E 5134
11.3%
R 4602
10.1%
O 3784
 
8.3%
L 3059
 
6.7%
I 2686
 
5.9%
Z 2548
 
5.6%
N 2411
 
5.3%
S 2340
 
5.1%
C 1732
 
3.8%
Other values (17) 11072
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6174
13.6%
E 5134
11.3%
R 4602
10.1%
O 3784
 
8.3%
L 3059
 
6.7%
I 2686
 
5.9%
Z 2548
 
5.6%
N 2411
 
5.3%
S 2340
 
5.1%
C 1732
 
3.8%
Other values (17) 11072
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6174
13.6%
E 5134
11.3%
R 4602
10.1%
O 3784
 
8.3%
L 3059
 
6.7%
I 2686
 
5.9%
Z 2548
 
5.6%
N 2411
 
5.3%
S 2340
 
5.1%
C 1732
 
3.8%
Other values (17) 11072
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6174
13.6%
E 5134
11.3%
R 4602
10.1%
O 3784
 
8.3%
L 3059
 
6.7%
I 2686
 
5.9%
Z 2548
 
5.6%
N 2411
 
5.3%
S 2340
 
5.1%
C 1732
 
3.8%
Other values (17) 11072
24.3%

SEGUNDO_APELLIDO
Text

Missing 

Distinct999
Distinct (%)14.4%
Missing15849
Missing (%)69.5%
Memory size178.2 KiB
2025-02-11T10:42:15.398310image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length18
Median length16
Mean length6.5959072
Min length1

Characters and Unicode

Total characters45769
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique471 ?
Unique (%)6.8%

Sample

1st rowCORONADO
2nd rowVAZQUEZ
3rd rowVAZQUEZ
4th rowVAZQUEZ
5th rowVAZQUEZ
ValueCountFrequency (%)
garcia 370
 
5.2%
hernandez 351
 
5.0%
gomez 234
 
3.3%
rodriguez 201
 
2.8%
alcaraz 149
 
2.1%
gonzalez 141
 
2.0%
martinez 137
 
1.9%
rivas 122
 
1.7%
lopez 113
 
1.6%
cruz 103
 
1.5%
Other values (975) 5138
72.8%
2025-02-11T10:42:15.889275image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 6960
15.2%
E 5062
11.1%
R 5014
11.0%
O 3434
 
7.5%
Z 2880
 
6.3%
I 2655
 
5.8%
N 2630
 
5.7%
L 2367
 
5.2%
S 2070
 
4.5%
G 1731
 
3.8%
Other values (25) 10966
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6960
15.2%
E 5062
11.1%
R 5014
11.0%
O 3434
 
7.5%
Z 2880
 
6.3%
I 2655
 
5.8%
N 2630
 
5.7%
L 2367
 
5.2%
S 2070
 
4.5%
G 1731
 
3.8%
Other values (25) 10966
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6960
15.2%
E 5062
11.1%
R 5014
11.0%
O 3434
 
7.5%
Z 2880
 
6.3%
I 2655
 
5.8%
N 2630
 
5.7%
L 2367
 
5.2%
S 2070
 
4.5%
G 1731
 
3.8%
Other values (25) 10966
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6960
15.2%
E 5062
11.1%
R 5014
11.0%
O 3434
 
7.5%
Z 2880
 
6.3%
I 2655
 
5.8%
N 2630
 
5.7%
L 2367
 
5.2%
S 2070
 
4.5%
G 1731
 
3.8%
Other values (25) 10966
24.0%

SEXO
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing15849
Missing (%)69.5%
Memory size178.2 KiB
M
4102 
F
2837 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6939
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 4102
 
18.0%
F 2837
 
12.4%
(Missing) 15849
69.5%

Length

2025-02-11T10:42:16.028308image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-11T10:42:16.158440image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
m 4102
59.1%
f 2837
40.9%

Most occurring characters

ValueCountFrequency (%)
M 4102
59.1%
F 2837
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 4102
59.1%
F 2837
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 4102
59.1%
F 2837
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 4102
59.1%
F 2837
40.9%

FECHA_DE_NACIMIENTO
Date

Missing 

Distinct2845
Distinct (%)41.0%
Missing15849
Missing (%)69.5%
Memory size178.2 KiB
Minimum1936-05-13 00:00:00
Maximum2004-10-24 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-11T10:42:16.295773image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:16.575123image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

RAZON_SOCIAL
Text

Missing 

Distinct1845
Distinct (%)11.6%
Missing6939
Missing (%)30.5%
Memory size178.2 KiB
2025-02-11T10:42:16.894678image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length84
Median length61
Mean length33.841504
Min length8

Characters and Unicode

Total characters536354
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique898 ?
Unique (%)5.7%

Sample

1st rowLAMINA DESPLEGADA SA DE CV
2nd rowCAFE EL MARINO
3rd rowCAFE EL MARINO
4th rowCAFE EL MARINO
5th rowSCHETTINO HERMANOS, S. DE R.L. DE C.V.
ValueCountFrequency (%)
de 17233
 
18.4%
cv 6862
 
7.3%
sa 6490
 
6.9%
c.v 5928
 
6.3%
s.a 5038
 
5.4%
y 1912
 
2.0%
mexico 1889
 
2.0%
s 1878
 
2.0%
del 1791
 
1.9%
sofom 1280
 
1.4%
Other values (2805) 43548
46.4%
2025-02-11T10:42:17.444065image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79479
14.8%
E 53423
 
10.0%
A 46968
 
8.8%
S 39624
 
7.4%
C 33442
 
6.2%
. 31485
 
5.9%
O 29938
 
5.6%
R 29787
 
5.6%
I 28745
 
5.4%
D 27533
 
5.1%
Other values (70) 135930
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 536354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
79479
14.8%
E 53423
 
10.0%
A 46968
 
8.8%
S 39624
 
7.4%
C 33442
 
6.2%
. 31485
 
5.9%
O 29938
 
5.6%
R 29787
 
5.6%
I 28745
 
5.4%
D 27533
 
5.1%
Other values (70) 135930
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 536354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
79479
14.8%
E 53423
 
10.0%
A 46968
 
8.8%
S 39624
 
7.4%
C 33442
 
6.2%
. 31485
 
5.9%
O 29938
 
5.6%
R 29787
 
5.6%
I 28745
 
5.4%
D 27533
 
5.1%
Other values (70) 135930
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 536354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
79479
14.8%
E 53423
 
10.0%
A 46968
 
8.8%
S 39624
 
7.4%
C 33442
 
6.2%
. 31485
 
5.9%
O 29938
 
5.6%
R 29787
 
5.6%
I 28745
 
5.4%
D 27533
 
5.1%
Other values (70) 135930
25.3%

NOMBRE_COMERCIAL
Text

Missing 

Distinct1845
Distinct (%)11.6%
Missing6939
Missing (%)30.5%
Memory size178.2 KiB
2025-02-11T10:42:17.722362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length117
Median length61
Mean length31.100259
Min length5

Characters and Unicode

Total characters492908
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique898 ?
Unique (%)5.7%

Sample

1st rowLAMINA DESPLEGADA SA DE CV
2nd rowCAFE EL MARINO
3rd rowCAFE EL MARINO
4th rowCAFE EL MARINO
5th rowSCHETTINO HERMANOS, S. DE R.L. DE C.V.
ValueCountFrequency (%)
de 14866
 
17.6%
cv 5680
 
6.7%
sa 5582
 
6.6%
c.v 4836
 
5.7%
s.a 4293
 
5.1%
y 1906
 
2.3%
mexico 1890
 
2.2%
del 1803
 
2.1%
s 1788
 
2.1%
r.l 1004
 
1.2%
Other values (2798) 40906
48.4%
2025-02-11T10:42:18.204215image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69962
14.2%
E 50017
 
10.1%
A 44770
 
9.1%
S 35982
 
7.3%
C 31277
 
6.3%
R 28653
 
5.8%
I 28295
 
5.7%
O 27705
 
5.6%
D 25219
 
5.1%
. 23755
 
4.8%
Other values (70) 127273
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 492908
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
69962
14.2%
E 50017
 
10.1%
A 44770
 
9.1%
S 35982
 
7.3%
C 31277
 
6.3%
R 28653
 
5.8%
I 28295
 
5.7%
O 27705
 
5.6%
D 25219
 
5.1%
. 23755
 
4.8%
Other values (70) 127273
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 492908
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
69962
14.2%
E 50017
 
10.1%
A 44770
 
9.1%
S 35982
 
7.3%
C 31277
 
6.3%
R 28653
 
5.8%
I 28295
 
5.7%
O 27705
 
5.6%
D 25219
 
5.1%
. 23755
 
4.8%
Other values (70) 127273
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 492908
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
69962
14.2%
E 50017
 
10.1%
A 44770
 
9.1%
S 35982
 
7.3%
C 31277
 
6.3%
R 28653
 
5.8%
I 28295
 
5.7%
O 27705
 
5.6%
D 25219
 
5.1%
. 23755
 
4.8%
Other values (70) 127273
25.8%

NO_EMPLEADOS
Real number (ℝ)

High correlation  Skewed 

Distinct301
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.65451
Minimum0
Maximum130000
Zeros141
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:18.335267image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median20
Q380
95-th percentile850
Maximum130000
Range130000
Interquartile range (IQR)75

Descriptive statistics

Standard deviation2872.6355
Coefficient of variation (CV)10.199146
Kurtosis1168.5826
Mean281.65451
Median Absolute Deviation (MAD)19
Skewness31.642032
Sum6418343
Variance8252034.8
MonotonicityNot monotonic
2025-02-11T10:42:18.482887image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4273
18.8%
500 1545
 
6.8%
10 1542
 
6.8%
5 1297
 
5.7%
20 950
 
4.2%
50 770
 
3.4%
354 769
 
3.4%
1270 742
 
3.3%
15 586
 
2.6%
8 507
 
2.2%
Other values (291) 9807
43.0%
ValueCountFrequency (%)
0 141
 
0.6%
1 4273
18.8%
2 263
 
1.2%
3 503
 
2.2%
4 185
 
0.8%
5 1297
 
5.7%
6 328
 
1.4%
7 351
 
1.5%
8 507
 
2.2%
9 252
 
1.1%
ValueCountFrequency (%)
130000 1
 
< 0.1%
128475 1
 
< 0.1%
120800 3
 
< 0.1%
93184 8
< 0.1%
42000 3
 
< 0.1%
32347 17
0.1%
26064 17
0.1%
22241 1
 
< 0.1%
15000 4
 
< 0.1%
14000 8
< 0.1%

VENTAS_NETAS
Real number (ℝ)

High correlation  Skewed 

Distinct1274
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2019786 × 109
Minimum0
Maximum1.6308529 × 1012
Zeros141
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:18.639815image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5001
Q1113892
median9000507
Q393285120
95-th percentile4.187462 × 109
Maximum1.6308529 × 1012
Range1.6308529 × 1012
Interquartile range (IQR)93171228

Descriptive statistics

Standard deviation2.2504494 × 1010
Coefficient of variation (CV)18.722874
Kurtosis3851.8161
Mean1.2019786 × 109
Median Absolute Deviation (MAD)8995506
Skewness58.252748
Sum2.7390688 × 1013
Variance5.0645225 × 1020
MonotonicityNot monotonic
2025-02-11T10:42:18.825891image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5001 3452
 
15.1%
50000 1206
 
5.3%
9892253586 742
 
3.3%
4187462000 698
 
3.1%
150000 556
 
2.4%
29106853 531
 
2.3%
93285120 445
 
2.0%
5000 423
 
1.9%
121456183 374
 
1.6%
581556532.1 265
 
1.2%
Other values (1264) 14096
61.9%
ValueCountFrequency (%)
0 141
 
0.6%
1 102
 
0.4%
100 2
 
< 0.1%
1800 2
 
< 0.1%
2812.57 19
 
0.1%
5000 423
 
1.9%
5001 3452
15.1%
5500 7
 
< 0.1%
7000 1
 
< 0.1%
7500 1
 
< 0.1%
ValueCountFrequency (%)
1.63085289 × 10123
 
< 0.1%
6.43404732 × 10118
< 0.1%
1.96607 × 10113
 
< 0.1%
4.62487704 × 10101
 
< 0.1%
3.965681318 × 101016
0.1%
3.55850007 × 10101
 
< 0.1%
3.3430939 × 10102
 
< 0.1%
3.1133 × 10109
< 0.1%
3.10379058 × 101017
0.1%
2.778811182 × 10101
 
< 0.1%

PAIS
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
MEXICO
22785 
ALEMANIA
 
2
ESTADOS UNIDOS
 
1

Length

Max length14
Median length6
Mean length6.0005266
Min length6

Characters and Unicode

Total characters136740
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMEXICO
2nd rowMEXICO
3rd rowMEXICO
4th rowMEXICO
5th rowMEXICO

Common Values

ValueCountFrequency (%)
MEXICO 22785
> 99.9%
ALEMANIA 2
 
< 0.1%
ESTADOS UNIDOS 1
 
< 0.1%

Length

2025-02-11T10:42:18.993556image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-11T10:42:19.109753image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
mexico 22785
> 99.9%
alemania 2
 
< 0.1%
estados 1
 
< 0.1%
unidos 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 22788
16.7%
I 22788
16.7%
M 22787
16.7%
O 22787
16.7%
X 22785
16.7%
C 22785
16.7%
A 7
 
< 0.1%
N 3
 
< 0.1%
S 3
 
< 0.1%
L 2
 
< 0.1%
Other values (4) 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 22788
16.7%
I 22788
16.7%
M 22787
16.7%
O 22787
16.7%
X 22785
16.7%
C 22785
16.7%
A 7
 
< 0.1%
N 3
 
< 0.1%
S 3
 
< 0.1%
L 2
 
< 0.1%
Other values (4) 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 22788
16.7%
I 22788
16.7%
M 22787
16.7%
O 22787
16.7%
X 22785
16.7%
C 22785
16.7%
A 7
 
< 0.1%
N 3
 
< 0.1%
S 3
 
< 0.1%
L 2
 
< 0.1%
Other values (4) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 22788
16.7%
I 22788
16.7%
M 22787
16.7%
O 22787
16.7%
X 22785
16.7%
C 22785
16.7%
A 7
 
< 0.1%
N 3
 
< 0.1%
S 3
 
< 0.1%
L 2
 
< 0.1%
Other values (4) 5
 
< 0.1%

NACIONALIDAD
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
MEXICANA
22785 
ALEMANA
 
2
ESTADOUNIDENSE
 
1

Length

Max length14
Median length8
Mean length8.0001755
Min length7

Characters and Unicode

Total characters182308
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMEXICANA
2nd rowMEXICANA
3rd rowMEXICANA
4th rowMEXICANA
5th rowMEXICANA

Common Values

ValueCountFrequency (%)
MEXICANA 22785
> 99.9%
ALEMANA 2
 
< 0.1%
ESTADOUNIDENSE 1
 
< 0.1%

Length

2025-02-11T10:42:19.251934image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-11T10:42:19.351586image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
mexicana 22785
> 99.9%
alemana 2
 
< 0.1%
estadounidense 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 45577
25.0%
E 22790
12.5%
N 22789
12.5%
M 22787
12.5%
I 22786
12.5%
X 22785
12.5%
C 22785
12.5%
L 2
 
< 0.1%
S 2
 
< 0.1%
D 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 45577
25.0%
E 22790
12.5%
N 22789
12.5%
M 22787
12.5%
I 22786
12.5%
X 22785
12.5%
C 22785
12.5%
L 2
 
< 0.1%
S 2
 
< 0.1%
D 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 45577
25.0%
E 22790
12.5%
N 22789
12.5%
M 22787
12.5%
I 22786
12.5%
X 22785
12.5%
C 22785
12.5%
L 2
 
< 0.1%
S 2
 
< 0.1%
D 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 45577
25.0%
E 22790
12.5%
N 22789
12.5%
M 22787
12.5%
I 22786
12.5%
X 22785
12.5%
C 22785
12.5%
L 2
 
< 0.1%
S 2
 
< 0.1%
D 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

DELEGACION_MUNICIPIO
Categorical

High correlation  Missing 

Distinct38
Distinct (%)5.0%
Missing22033
Missing (%)96.7%
Memory size178.2 KiB
MONTERREY
334 
CULIACAN
115 
SAN LUIS POTOSI
66 
PARRAS DE LA FUENTE
38 
ISLA MUJERES
37 
Other values (33)
165 

Length

Max length28
Median length26
Mean length10.2
Min length4

Characters and Unicode

Total characters7701
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.7%

Sample

1st rowSANTA CATARINA LA FAMA
2nd rowMAZATLAN
3rd rowMAZATLAN
4th rowMAZATLAN
5th rowORIZABA

Common Values

ValueCountFrequency (%)
MONTERREY 334
 
1.5%
CULIACAN 115
 
0.5%
SAN LUIS POTOSI 66
 
0.3%
PARRAS DE LA FUENTE 38
 
0.2%
ISLA MUJERES 37
 
0.2%
VERACRUZ 34
 
0.1%
TORREON 32
 
0.1%
GUADALAJARA 23
 
0.1%
IZTACALCO 13
 
0.1%
SALTO EL 7
 
< 0.1%
Other values (28) 56
 
0.2%
(Missing) 22033
96.7%

Length

2025-02-11T10:42:19.464169image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
monterrey 334
30.6%
culiacan 115
 
10.5%
san 67
 
6.1%
luis 66
 
6.0%
potosi 66
 
6.0%
de 47
 
4.3%
la 39
 
3.6%
parras 38
 
3.5%
fuente 38
 
3.5%
mujeres 37
 
3.4%
Other values (48) 246
22.5%

Most occurring characters

ValueCountFrequency (%)
E 986
12.8%
R 972
12.6%
A 746
9.7%
N 625
 
8.1%
O 607
 
7.9%
T 513
 
6.7%
M 390
 
5.1%
L 359
 
4.7%
U 351
 
4.6%
S 339
 
4.4%
Other values (17) 1813
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 986
12.8%
R 972
12.6%
A 746
9.7%
N 625
 
8.1%
O 607
 
7.9%
T 513
 
6.7%
M 390
 
5.1%
L 359
 
4.7%
U 351
 
4.6%
S 339
 
4.4%
Other values (17) 1813
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 986
12.8%
R 972
12.6%
A 746
9.7%
N 625
 
8.1%
O 607
 
7.9%
T 513
 
6.7%
M 390
 
5.1%
L 359
 
4.7%
U 351
 
4.6%
S 339
 
4.4%
Other values (17) 1813
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 986
12.8%
R 972
12.6%
A 746
9.7%
N 625
 
8.1%
O 607
 
7.9%
T 513
 
6.7%
M 390
 
5.1%
L 359
 
4.7%
U 351
 
4.6%
S 339
 
4.4%
Other values (17) 1813
23.5%

ENTIDAD_FEDERATIVA
Categorical

High correlation  Missing 

Distinct21
Distinct (%)2.8%
Missing22033
Missing (%)96.7%
Memory size178.2 KiB
NUEVO LEON
341 
SINALOA
119 
COAHUILA DE ZARAGOZA
70 
SAN LUIS POTOSI
66 
JALISCO
40 
Other values (16)
119 

Length

Max length20
Median length19
Mean length10.85298
Min length6

Characters and Unicode

Total characters8194
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.8%

Sample

1st rowNUEVO LEON
2nd rowSINALOA
3rd rowSINALOA
4th rowSINALOA
5th rowVERACRUZ

Common Values

ValueCountFrequency (%)
NUEVO LEON 341
 
1.5%
SINALOA 119
 
0.5%
COAHUILA DE ZARAGOZA 70
 
0.3%
SAN LUIS POTOSI 66
 
0.3%
JALISCO 40
 
0.2%
QUINTANA ROO 37
 
0.2%
VERACRUZ 36
 
0.2%
CIUDAD DE MEXICO 21
 
0.1%
GUANAJUATO 4
 
< 0.1%
MEXICO 3
 
< 0.1%
Other values (11) 18
 
0.1%
(Missing) 22033
96.7%

Length

2025-02-11T10:42:19.622309image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nuevo 341
23.5%
leon 341
23.5%
sinaloa 119
 
8.2%
de 92
 
6.3%
coahuila 70
 
4.8%
zaragoza 70
 
4.8%
san 66
 
4.6%
luis 66
 
4.6%
potosi 66
 
4.6%
jalisco 40
 
2.8%
Other values (19) 179
12.3%

Most occurring characters

ValueCountFrequency (%)
O 1227
15.0%
N 952
11.6%
A 872
10.6%
E 841
10.3%
695
8.5%
L 639
7.8%
U 593
7.2%
I 452
 
5.5%
V 377
 
4.6%
S 358
 
4.4%
Other values (15) 1188
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1227
15.0%
N 952
11.6%
A 872
10.6%
E 841
10.3%
695
8.5%
L 639
7.8%
U 593
7.2%
I 452
 
5.5%
V 377
 
4.6%
S 358
 
4.4%
Other values (15) 1188
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1227
15.0%
N 952
11.6%
A 872
10.6%
E 841
10.3%
695
8.5%
L 639
7.8%
U 593
7.2%
I 452
 
5.5%
V 377
 
4.6%
S 358
 
4.4%
Other values (15) 1188
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1227
15.0%
N 952
11.6%
A 872
10.6%
E 841
10.3%
695
8.5%
L 639
7.8%
U 593
7.2%
I 452
 
5.5%
V 377
 
4.6%
S 358
 
4.4%
Other values (15) 1188
14.5%

TIPO_SECTOR
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
INDUSTRIAS MANUFACTURERAS
9539 
COMERCIO
6168 
SERVICIOS COMUNALES Y SOCIALES (HOTELES Y RESTAURANTES)
2095 
SERVICIOS FINANCIEROS, DE ADMON Y ALQUILER DE BIENES MUEBLES
1924 
TRANSPORTES Y COMUNICACIONES
1840 
Other values (3)
1222 

Length

Max length60
Median length55
Mean length25.769089
Min length8

Characters and Unicode

Total characters587226
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDUSTRIAS MANUFACTURERAS
2nd rowINDUSTRIAS MANUFACTURERAS
3rd rowINDUSTRIAS MANUFACTURERAS
4th rowINDUSTRIAS MANUFACTURERAS
5th rowCOMERCIO

Common Values

ValueCountFrequency (%)
INDUSTRIAS MANUFACTURERAS 9539
41.9%
COMERCIO 6168
27.1%
SERVICIOS COMUNALES Y SOCIALES (HOTELES Y RESTAURANTES) 2095
 
9.2%
SERVICIOS FINANCIEROS, DE ADMON Y ALQUILER DE BIENES MUEBLES 1924
 
8.4%
TRANSPORTES Y COMUNICACIONES 1840
 
8.1%
CONSTRUCCION 1059
 
4.6%
MINERIA Y EXTRACCION DE PETROLEO 109
 
0.5%
ELECTRICIDAD Y AGUA 54
 
0.2%

Length

2025-02-11T10:42:19.767880image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-11T10:42:19.908705image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
industrias 9539
14.8%
manufactureras 9539
14.8%
y 8117
12.6%
comercio 6168
9.6%
servicios 4019
 
6.2%
de 3957
 
6.1%
comunales 2095
 
3.2%
sociales 2095
 
3.2%
hoteles 2095
 
3.2%
restaurantes 2095
 
3.2%
Other values (13) 14794
22.9%

Most occurring characters

ValueCountFrequency (%)
S 61576
10.5%
A 56368
9.6%
E 52021
8.9%
R 51962
8.8%
I 48249
 
8.2%
41725
 
7.1%
C 41031
 
7.0%
U 39608
 
6.7%
N 38820
 
6.6%
O 34453
 
5.9%
Other values (16) 121413
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 587226
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 61576
10.5%
A 56368
9.6%
E 52021
8.9%
R 51962
8.8%
I 48249
 
8.2%
41725
 
7.1%
C 41031
 
7.0%
U 39608
 
6.7%
N 38820
 
6.6%
O 34453
 
5.9%
Other values (16) 121413
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 587226
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 61576
10.5%
A 56368
9.6%
E 52021
8.9%
R 51962
8.8%
I 48249
 
8.2%
41725
 
7.1%
C 41031
 
7.0%
U 39608
 
6.7%
N 38820
 
6.6%
O 34453
 
5.9%
Other values (16) 121413
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 587226
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 61576
10.5%
A 56368
9.6%
E 52021
8.9%
R 51962
8.8%
I 48249
 
8.2%
41725
 
7.1%
C 41031
 
7.0%
U 39608
 
6.7%
N 38820
 
6.6%
O 34453
 
5.9%
Other values (16) 121413
20.7%

TIPO_SUBSECTOR
Categorical

High correlation 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
COMERCIO AL POR MAYOR
4438 
PRODUCTOS METALICOS ,MAQUINARIA Y EQUIPO.(I) INSTRUMENTOS Q
3874 
PRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACO
2692 
TRANSPORTE FERROVIARIO, METRO, TRANVIAS Y TROLEBUSES
1819 
SERVICIOS FINANCIEROS DE SEGUROS Y FINANZAS
1708 
Other values (24)
8257 

Length

Max length60
Median length58
Mean length38.843909
Min length12

Characters and Unicode

Total characters885175
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDUSTRIAS METALICAS BASICAS
2nd rowPRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACO
3rd rowPRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACO
4th rowPRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACO
5th rowCOMERCIO AL POR MAYOR

Common Values

ValueCountFrequency (%)
COMERCIO AL POR MAYOR 4438
19.5%
PRODUCTOS METALICOS ,MAQUINARIA Y EQUIPO.(I) INSTRUMENTOS Q 3874
17.0%
PRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACO 2692
11.8%
TRANSPORTE FERROVIARIO, METRO, TRANVIAS Y TROLEBUSES 1819
8.0%
SERVICIOS FINANCIEROS DE SEGUROS Y FINANZAS 1708
 
7.5%
COMERCIO AL POR MENOR 1575
 
6.9%
CONSTRUCCION 1059
 
4.6%
OTRAS INDUSTRIAS MANUFACTURERAS 1007
 
4.4%
SERVICIOS DE REPARACION Y MANTENIMIENTO 919
 
4.0%
SERVICIOS PROFESIONALES, TECNICOS ESPECIALIZAOS 873
 
3.8%
Other values (19) 2824
12.4%

Length

2025-02-11T10:42:20.116669image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 13279
 
11.3%
productos 7887
 
6.7%
comercio 6013
 
5.1%
por 6013
 
5.1%
al 6013
 
5.1%
de 5432
 
4.6%
mayor 4438
 
3.8%
metalicos 4045
 
3.4%
maquinaria 3874
 
3.3%
equipo.(i 3874
 
3.3%
Other values (81) 56695
48.2%

Most occurring characters

ValueCountFrequency (%)
94775
10.7%
O 86173
 
9.7%
I 77029
 
8.7%
R 69908
 
7.9%
A 69100
 
7.8%
E 67483
 
7.6%
S 64275
 
7.3%
T 47078
 
5.3%
C 45863
 
5.2%
N 40889
 
4.6%
Other values (18) 222602
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 885175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
94775
10.7%
O 86173
 
9.7%
I 77029
 
8.7%
R 69908
 
7.9%
A 69100
 
7.8%
E 67483
 
7.6%
S 64275
 
7.3%
T 47078
 
5.3%
C 45863
 
5.2%
N 40889
 
4.6%
Other values (18) 222602
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 885175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
94775
10.7%
O 86173
 
9.7%
I 77029
 
8.7%
R 69908
 
7.9%
A 69100
 
7.8%
E 67483
 
7.6%
S 64275
 
7.3%
T 47078
 
5.3%
C 45863
 
5.2%
N 40889
 
4.6%
Other values (18) 222602
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 885175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
94775
10.7%
O 86173
 
9.7%
I 77029
 
8.7%
R 69908
 
7.9%
A 69100
 
7.8%
E 67483
 
7.6%
S 64275
 
7.3%
T 47078
 
5.3%
C 45863
 
5.2%
N 40889
 
4.6%
Other values (18) 222602
25.1%
Distinct90
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:20.448973image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length60
Median length59
Mean length48.957785
Min length11

Characters and Unicode

Total characters1115650
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowINDUSTRIAS BASICAS DE METALES NO FERROSOS. (I) EL TRATAMIENT
2nd rowBENEFICIO Y MOLIENDA DE CEREALES Y OTROS PRODUCTOS AGRICOLAS
3rd rowBENEFICIO Y MOLIENDA DE CEREALES Y OTROS PRODUCTOS AGRICOLAS
4th rowBENEFICIO Y MOLIENDA DE CEREALES Y OTROS PRODUCTOS AGRICOLAS
5th rowCOMERCIO DE PRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACO AL POR
ValueCountFrequency (%)
de 22004
 
13.8%
y 10839
 
6.8%
productos 8805
 
5.5%
alimenticios 6682
 
4.2%
por 6349
 
4.0%
comercio 5938
 
3.7%
al 5937
 
3.7%
reparacion 4566
 
2.9%
maquinaria 4352
 
2.7%
fabricacion 4301
 
2.7%
Other values (213) 79878
50.0%
2025-02-11T10:42:20.942168image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
137991
12.4%
O 106238
 
9.5%
A 105808
 
9.5%
I 96030
 
8.6%
E 95525
 
8.6%
R 75952
 
6.8%
C 70732
 
6.3%
S 62418
 
5.6%
N 60677
 
5.4%
T 38206
 
3.4%
Other values (21) 266073
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1115650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
137991
12.4%
O 106238
 
9.5%
A 105808
 
9.5%
I 96030
 
8.6%
E 95525
 
8.6%
R 75952
 
6.8%
C 70732
 
6.3%
S 62418
 
5.6%
N 60677
 
5.4%
T 38206
 
3.4%
Other values (21) 266073
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1115650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
137991
12.4%
O 106238
 
9.5%
A 105808
 
9.5%
I 96030
 
8.6%
E 95525
 
8.6%
R 75952
 
6.8%
C 70732
 
6.3%
S 62418
 
5.6%
N 60677
 
5.4%
T 38206
 
3.4%
Other values (21) 266073
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1115650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
137991
12.4%
O 106238
 
9.5%
A 105808
 
9.5%
I 96030
 
8.6%
E 95525
 
8.6%
R 75952
 
6.8%
C 70732
 
6.3%
S 62418
 
5.6%
N 60677
 
5.4%
T 38206
 
3.4%
Other values (21) 266073
23.8%
Distinct304
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:21.290546image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length60
Median length60
Mean length56.375066
Min length5

Characters and Unicode

Total characters1284675
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)0.2%

Sample

1st rowFUNDICION, LAMINACION, EXTRUSION, REFINACION Y/O ESTIRAJE DE
2nd rowBENEFICIO DE CAFE
3rd rowBENEFICIO DE CAFE
4th rowBENEFICIO DE CAFE
5th rowCHILE SECO Y ESPECIAS (I) PASTA PARA MOLE Y SIMILARES
ValueCountFrequency (%)
de 28728
 
14.9%
y 15652
 
8.1%
i 8999
 
4.7%
fabricacion 5387
 
2.8%
ref 4276
 
2.2%
aparatos 3499
 
1.8%
equipos 3454
 
1.8%
aire 3453
 
1.8%
acondicionado 3453
 
1.8%
no 3087
 
1.6%
Other values (784) 112311
58.4%
2025-02-11T10:42:21.800156image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
170197
13.2%
A 133794
10.4%
E 119477
9.3%
O 109405
 
8.5%
I 101190
 
7.9%
R 95668
 
7.4%
S 76778
 
6.0%
C 73025
 
5.7%
N 67393
 
5.2%
D 58044
 
4.5%
Other values (26) 279704
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1284675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
170197
13.2%
A 133794
10.4%
E 119477
9.3%
O 109405
 
8.5%
I 101190
 
7.9%
R 95668
 
7.4%
S 76778
 
6.0%
C 73025
 
5.7%
N 67393
 
5.2%
D 58044
 
4.5%
Other values (26) 279704
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1284675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
170197
13.2%
A 133794
10.4%
E 119477
9.3%
O 109405
 
8.5%
I 101190
 
7.9%
R 95668
 
7.4%
S 76778
 
6.0%
C 73025
 
5.7%
N 67393
 
5.2%
D 58044
 
4.5%
Other values (26) 279704
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1284675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
170197
13.2%
A 133794
10.4%
E 119477
9.3%
O 109405
 
8.5%
I 101190
 
7.9%
R 95668
 
7.4%
S 76778
 
6.0%
C 73025
 
5.7%
N 67393
 
5.2%
D 58044
 
4.5%
Other values (26) 279704
21.8%

NUMERO_CONTRATO
Real number (ℝ)

High correlation 

Distinct22735
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39518177
Minimum5643752
Maximum40709381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:21.950515image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum5643752
5-th percentile34459794
Q140379620
median40573686
Q340659959
95-th percentile40700819
Maximum40709381
Range35065629
Interquartile range (IQR)280339.75

Descriptive statistics

Standard deviation2641404.1
Coefficient of variation (CV)0.066840232
Kurtosis26.954456
Mean39518177
Median Absolute Deviation (MAD)113362
Skewness-4.1013988
Sum9.0054022 × 1011
Variance6.9770157 × 1012
MonotonicityNot monotonic
2025-02-11T10:42:22.118529image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40709267 11
 
< 0.1%
40709252 5
 
< 0.1%
24231558 5
 
< 0.1%
35124289 5
 
< 0.1%
34170179 5
 
< 0.1%
34157967 4
 
< 0.1%
35199909 4
 
< 0.1%
40709255 4
 
< 0.1%
40705513 3
 
< 0.1%
34236669 3
 
< 0.1%
Other values (22725) 22739
99.8%
ValueCountFrequency (%)
5643752 1
< 0.1%
7406253 1
< 0.1%
7870719 1
< 0.1%
7879529 1
< 0.1%
7879715 1
< 0.1%
8058557 1
< 0.1%
8098586 1
< 0.1%
8602707 1
< 0.1%
8622186 1
< 0.1%
8622187 1
< 0.1%
ValueCountFrequency (%)
40709381 1
< 0.1%
40709380 1
< 0.1%
40709379 1
< 0.1%
40709378 1
< 0.1%
40709377 1
< 0.1%
40709376 1
< 0.1%
40709375 1
< 0.1%
40709374 1
< 0.1%
40709373 1
< 0.1%
40709372 1
< 0.1%

NUMERO_PRESTAMO
Real number (ℝ)

High correlation 

Distinct22584
Distinct (%)100.0%
Missing204
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean4.0925883 × 108
Minimum71740515
Maximum4.2087615 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:22.391457image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum71740515
5-th percentile3.5894232 × 108
Q14.1761063 × 108
median4.1953925 × 108
Q34.2038686 × 108
95-th percentile4.2078765 × 108
Maximum4.2087615 × 108
Range3.4913563 × 108
Interquartile range (IQR)2776224.5

Descriptive statistics

Standard deviation26146913
Coefficient of variation (CV)0.063888452
Kurtosis27.837987
Mean4.0925883 × 108
Median Absolute Deviation (MAD)1109541
Skewness-4.180004
Sum9.2427014 × 1012
Variance6.8366107 × 1014
MonotonicityNot monotonic
2025-02-11T10:42:22.575231image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405327964 1
 
< 0.1%
420377346 1
 
< 0.1%
355828125 1
 
< 0.1%
359792148 1
 
< 0.1%
359792193 1
 
< 0.1%
420279587 1
 
< 0.1%
420265010 1
 
< 0.1%
420265243 1
 
< 0.1%
379031708 1
 
< 0.1%
420562556 1
 
< 0.1%
Other values (22574) 22574
99.1%
(Missing) 204
 
0.9%
ValueCountFrequency (%)
71740515 1
< 0.1%
89357139 1
< 0.1%
93998091 1
< 0.1%
94086378 1
< 0.1%
94088303 1
< 0.1%
95876638 1
< 0.1%
96282778 1
< 0.1%
101313464 1
< 0.1%
101508094 1
< 0.1%
101508101 1
< 0.1%
ValueCountFrequency (%)
420876146 1
< 0.1%
420876137 1
< 0.1%
420876128 1
< 0.1%
420876119 1
< 0.1%
420876100 1
< 0.1%
420876093 1
< 0.1%
420876084 1
< 0.1%
420876075 1
< 0.1%
420876066 1
< 0.1%
420876057 1
< 0.1%
Distinct1141
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
Minimum2007-04-24 00:00:00
Maximum2025-01-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-11T10:42:22.717352image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:22.864077image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AÑO_REGISTRO
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2023.4406
Minimum2007
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:22.981746image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2021
Q12024
median2024
Q32024
95-th percentile2024
Maximum2025
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4373069
Coefficient of variation (CV)0.00071032819
Kurtosis21.784156
Mean2023.4406
Median Absolute Deviation (MAD)0
Skewness-3.9161485
Sum46110164
Variance2.0658511
MonotonicityNot monotonic
2025-02-11T10:42:23.114898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2024 18206
79.9%
2022 1304
 
5.7%
2021 1264
 
5.5%
2023 1151
 
5.1%
2020 299
 
1.3%
2018 177
 
0.8%
2017 110
 
0.5%
2019 103
 
0.5%
2016 82
 
0.4%
2015 26
 
0.1%
Other values (9) 66
 
0.3%
ValueCountFrequency (%)
2007 1
 
< 0.1%
2008 6
 
< 0.1%
2009 10
 
< 0.1%
2010 8
 
< 0.1%
2011 3
 
< 0.1%
2012 4
 
< 0.1%
2013 17
 
0.1%
2014 12
 
0.1%
2015 26
 
0.1%
2016 82
0.4%
ValueCountFrequency (%)
2025 5
 
< 0.1%
2024 18206
79.9%
2023 1151
 
5.1%
2022 1304
 
5.7%
2021 1264
 
5.5%
2020 299
 
1.3%
2019 103
 
0.5%
2018 177
 
0.8%
2017 110
 
0.5%
2016 82
 
0.4%

MES_REGISTRO
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9525189
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:23.260725image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median10
Q310
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.325327
Coefficient of variation (CV)0.25973997
Kurtosis2.7367869
Mean8.9525189
Median Absolute Deviation (MAD)1
Skewness-1.7681332
Sum204010
Variance5.4071455
MonotonicityNot monotonic
2025-02-11T10:42:23.389835image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 9082
39.9%
9 3929
17.2%
11 3430
 
15.1%
8 2280
 
10.0%
12 608
 
2.7%
6 574
 
2.5%
3 568
 
2.5%
7 553
 
2.4%
5 497
 
2.2%
4 454
 
2.0%
Other values (2) 813
 
3.6%
ValueCountFrequency (%)
1 383
 
1.7%
2 430
 
1.9%
3 568
 
2.5%
4 454
 
2.0%
5 497
 
2.2%
6 574
 
2.5%
7 553
 
2.4%
8 2280
 
10.0%
9 3929
17.2%
10 9082
39.9%
ValueCountFrequency (%)
12 608
 
2.7%
11 3430
 
15.1%
10 9082
39.9%
9 3929
17.2%
8 2280
 
10.0%
7 553
 
2.4%
6 574
 
2.5%
5 497
 
2.2%
4 454
 
2.0%
3 568
 
2.5%

DIA_REGISTRO
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.4835
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:23.517514image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median18
Q325
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.5216959
Coefficient of variation (CV)0.57765013
Kurtosis-1.2835406
Mean16.4835
Median Absolute Deviation (MAD)8
Skewness-0.17917262
Sum375626
Variance90.662693
MonotonicityNot monotonic
2025-02-11T10:42:23.642933image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1661
 
7.3%
4 1588
 
7.0%
18 1285
 
5.6%
23 1261
 
5.5%
30 1248
 
5.5%
25 1064
 
4.7%
11 1060
 
4.7%
21 964
 
4.2%
28 940
 
4.1%
5 770
 
3.4%
Other values (21) 10947
48.0%
ValueCountFrequency (%)
1 1661
7.3%
2 468
 
2.1%
3 384
 
1.7%
4 1588
7.0%
5 770
3.4%
6 345
 
1.5%
7 344
 
1.5%
8 269
 
1.2%
9 476
 
2.1%
10 698
3.1%
ValueCountFrequency (%)
31 643
2.8%
30 1248
5.5%
29 632
2.8%
28 940
4.1%
27 743
3.3%
26 725
3.2%
25 1064
4.7%
24 670
2.9%
23 1261
5.5%
22 766
3.4%
Distinct1117
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:24.027827image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters432972
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique381 ?
Unique (%)1.7%

Sample

1st row2025-04-16 00:00:00
2nd row2025-04-03 00:00:00
3rd row2025-03-03 00:00:00
4th row2025-02-17 00:00:00
5th row2025-02-11 00:00:00
ValueCountFrequency (%)
00:00:00 22788
50.0%
2025-03-04 2451
 
5.4%
2025-02-07 1303
 
2.9%
2025-02-11 950
 
2.1%
2025-02-13 913
 
2.0%
2025-02-26 879
 
1.9%
2025-02-17 831
 
1.8%
2025-02-20 827
 
1.8%
2025-02-24 715
 
1.6%
2025-03-06 625
 
1.4%
Other values (1108) 13294
29.2%
2025-02-11T10:42:24.540519image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 191152
44.1%
2 62047
 
14.3%
- 45576
 
10.5%
: 45576
 
10.5%
22788
 
5.3%
5 19028
 
4.4%
1 14014
 
3.2%
3 10483
 
2.4%
7 6741
 
1.6%
4 6105
 
1.4%
Other values (3) 9462
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 432972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 191152
44.1%
2 62047
 
14.3%
- 45576
 
10.5%
: 45576
 
10.5%
22788
 
5.3%
5 19028
 
4.4%
1 14014
 
3.2%
3 10483
 
2.4%
7 6741
 
1.6%
4 6105
 
1.4%
Other values (3) 9462
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 432972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 191152
44.1%
2 62047
 
14.3%
- 45576
 
10.5%
: 45576
 
10.5%
22788
 
5.3%
5 19028
 
4.4%
1 14014
 
3.2%
3 10483
 
2.4%
7 6741
 
1.6%
4 6105
 
1.4%
Other values (3) 9462
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 432972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 191152
44.1%
2 62047
 
14.3%
- 45576
 
10.5%
: 45576
 
10.5%
22788
 
5.3%
5 19028
 
4.4%
1 14014
 
3.2%
3 10483
 
2.4%
7 6741
 
1.6%
4 6105
 
1.4%
Other values (3) 9462
 
2.2%

AÑO_VENCIMIENTO
Real number (ℝ)

High correlation  Skewed 

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2025.7238
Minimum2025
Maximum2264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:24.666017image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2025
5-th percentile2025
Q12025
median2025
Q32025
95-th percentile2029
Maximum2264
Range239
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.5185768
Coefficient of variation (CV)0.0012432972
Kurtosis3525.5523
Mean2025.7238
Median Absolute Deviation (MAD)0
Skewness39.37349
Sum46162195
Variance6.3432291
MonotonicityNot monotonic
2025-02-11T10:42:24.797623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2025 17613
77.3%
2026 1569
 
6.9%
2027 1393
 
6.1%
2028 837
 
3.7%
2029 744
 
3.3%
2038 227
 
1.0%
2031 81
 
0.4%
2032 58
 
0.3%
2030 49
 
0.2%
2035 46
 
0.2%
Other values (11) 171
 
0.8%
ValueCountFrequency (%)
2025 17613
77.3%
2026 1569
 
6.9%
2027 1393
 
6.1%
2028 837
 
3.7%
2029 744
 
3.3%
2030 49
 
0.2%
2031 81
 
0.4%
2032 58
 
0.3%
2033 37
 
0.2%
2034 36
 
0.2%
ValueCountFrequency (%)
2264 1
 
< 0.1%
2056 3
 
< 0.1%
2049 2
 
< 0.1%
2045 2
 
< 0.1%
2043 1
 
< 0.1%
2041 1
 
< 0.1%
2039 7
 
< 0.1%
2038 227
1.0%
2037 42
 
0.2%
2036 39
 
0.2%

MES_VENCIMIENTO
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6045726
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:24.922855image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5605907
Coefficient of variation (CV)0.7103729
Kurtosis2.7438479
Mean3.6045726
Median Absolute Deviation (MAD)1
Skewness1.9242011
Sum82141
Variance6.5566245
MonotonicityNot monotonic
2025-02-11T10:42:25.044581image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 9666
42.4%
3 6804
29.9%
4 1713
 
7.5%
9 619
 
2.7%
6 565
 
2.5%
5 532
 
2.3%
7 515
 
2.3%
12 504
 
2.2%
11 489
 
2.1%
8 470
 
2.1%
Other values (2) 911
 
4.0%
ValueCountFrequency (%)
1 442
 
1.9%
2 9666
42.4%
3 6804
29.9%
4 1713
 
7.5%
5 532
 
2.3%
6 565
 
2.5%
7 515
 
2.3%
8 470
 
2.1%
9 619
 
2.7%
10 469
 
2.1%
ValueCountFrequency (%)
12 504
 
2.2%
11 489
 
2.1%
10 469
 
2.1%
9 619
 
2.7%
8 470
 
2.1%
7 515
 
2.3%
6 565
 
2.5%
5 532
 
2.3%
4 1713
 
7.5%
3 6804
29.9%

DIA_VENCIMIENTO
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.57052
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:25.196378image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median14
Q321
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5831265
Coefficient of variation (CV)0.58907484
Kurtosis-1.1188373
Mean14.57052
Median Absolute Deviation (MAD)7
Skewness0.22987536
Sum332033
Variance73.670061
MonotonicityNot monotonic
2025-02-11T10:42:25.355676image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4 2801
 
12.3%
17 2300
 
10.1%
7 1617
 
7.1%
13 1570
 
6.9%
10 1426
 
6.3%
20 1156
 
5.1%
11 1138
 
5.0%
29 1070
 
4.7%
26 978
 
4.3%
27 874
 
3.8%
Other values (21) 7858
34.5%
ValueCountFrequency (%)
1 633
 
2.8%
2 108
 
0.5%
3 843
 
3.7%
4 2801
12.3%
5 129
 
0.6%
6 682
 
3.0%
7 1617
7.1%
8 60
 
0.3%
9 141
 
0.6%
10 1426
6.3%
ValueCountFrequency (%)
31 334
 
1.5%
30 197
 
0.9%
29 1070
4.7%
28 457
2.0%
27 874
3.8%
26 978
4.3%
25 257
 
1.1%
24 869
3.8%
23 75
 
0.3%
22 73
 
0.3%

PRODUCTO
Categorical

High correlation  Imbalance 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
2DO. PISO, M.N. HABILITACION O AVIO
15438 
CREDITO SIMPLE FIDE
3446 
SIMPLE SIN GARANTIA REAL M.N.
1558 
DESCUENTOS MONEDA NACIONAL
 
778
2DO. PISO, M.N. SIMPLE
 
399
Other values (21)
 
1169

Length

Max length40
Median length35
Mean length31.232008
Min length12

Characters and Unicode

Total characters711715
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row2DO. PISO, M.N. SIMPLE
2nd row2DO. PISO, M.N. HABILITACION O AVIO
3rd row2DO. PISO, M.N. HABILITACION O AVIO
4th row2DO. PISO, M.N. HABILITACION O AVIO
5th row2DO. PISO, M.N. HABILITACION O AVIO

Common Values

ValueCountFrequency (%)
2DO. PISO, M.N. HABILITACION O AVIO 15438
67.7%
CREDITO SIMPLE FIDE 3446
 
15.1%
SIMPLE SIN GARANTIA REAL M.N. 1558
 
6.8%
DESCUENTOS MONEDA NACIONAL 778
 
3.4%
2DO. PISO, M.N. SIMPLE 399
 
1.8%
2DO. PISO, DLLS. HABILITACION O AVIO 396
 
1.7%
SIMPLE SIN GARANTIA REAL DLS. 239
 
1.0%
MANDATOS DLLS. 217
 
1.0%
HIPOTECARIO PARA VIVIENDA 67
 
0.3%
2DO. PISO, M.N. REFACCIONARIO MAQ. Y EQ. 49
 
0.2%
Other values (16) 201
 
0.9%

Length

2025-02-11T10:42:25.512220image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m.n 17499
14.6%
2do 16294
13.6%
piso 16294
13.6%
habilitacion 15834
13.2%
o 15834
13.2%
avio 15834
13.2%
simple 5666
 
4.7%
credito 3446
 
2.9%
fide 3446
 
2.9%
sin 1801
 
1.5%
Other values (34) 7778
6.5%

Most occurring characters

ValueCountFrequency (%)
I 97186
13.7%
96938
13.6%
O 86606
12.2%
A 58027
 
8.2%
. 52301
 
7.3%
N 40532
 
5.7%
S 26651
 
3.7%
D 26068
 
3.7%
L 25655
 
3.6%
M 24355
 
3.4%
Other values (17) 177396
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 711715
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 97186
13.7%
96938
13.6%
O 86606
12.2%
A 58027
 
8.2%
. 52301
 
7.3%
N 40532
 
5.7%
S 26651
 
3.7%
D 26068
 
3.7%
L 25655
 
3.6%
M 24355
 
3.4%
Other values (17) 177396
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 711715
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 97186
13.7%
96938
13.6%
O 86606
12.2%
A 58027
 
8.2%
. 52301
 
7.3%
N 40532
 
5.7%
S 26651
 
3.7%
D 26068
 
3.7%
L 25655
 
3.6%
M 24355
 
3.4%
Other values (17) 177396
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 711715
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 97186
13.7%
96938
13.6%
O 86606
12.2%
A 58027
 
8.2%
. 52301
 
7.3%
N 40532
 
5.7%
S 26651
 
3.7%
D 26068
 
3.7%
L 25655
 
3.6%
M 24355
 
3.4%
Other values (17) 177396
24.9%

DESC_LINEA_FINANCIERA
Categorical

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
2DO. PISO, BANCARIO
10125 
2DO PISO NO BANC C/AVAL BANC
5324 
DIRECTO CON DOCUMENTO
5263 
2DO. PISO, SIN AVAL BANCARIO
 
845
DIRECTO SIN DOCUMENTO (INFONAVIT)
 
773
Other values (7)
 
458

Length

Max length39
Median length33
Mean length22.507416
Min length17

Characters and Unicode

Total characters512899
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2DO PISO NO BANC C/AVAL BANC
2nd row2DO. PISO, SIN AVAL BANCARIO
3rd row2DO. PISO, SIN AVAL BANCARIO
4th row2DO. PISO, SIN AVAL BANCARIO
5th row2DO. PISO, BANCARIO

Common Values

ValueCountFrequency (%)
2DO. PISO, BANCARIO 10125
44.4%
2DO PISO NO BANC C/AVAL BANC 5324
23.4%
DIRECTO CON DOCUMENTO 5263
23.1%
2DO. PISO, SIN AVAL BANCARIO 845
 
3.7%
DIRECTO SIN DOCUMENTO (INFONAVIT) 773
 
3.4%
DIRECTO SIN DOCUMENTO 212
 
0.9%
CREDITOS PARA LA VIVIENDA CON DOCUMENTO 74
 
0.3%
CREDITOS AL CONSUMO CON DOCUMENTO 68
 
0.3%
INTERBANCARIO EN EL PAIS 52
 
0.2%
AGENCIAS OTROS BA 48
 
0.2%
Other values (2) 4
 
< 0.1%

Length

2025-02-11T10:42:25.630618image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2do 16294
18.7%
piso 16294
18.7%
bancario 10970
12.6%
banc 10648
12.2%
documento 6392
 
7.3%
directo 6250
 
7.2%
con 5407
 
6.2%
no 5324
 
6.1%
c/aval 5324
 
6.1%
sin 1832
 
2.1%
Other values (19) 2478
 
2.8%

Most occurring characters

ValueCountFrequency (%)
O 74526
14.5%
64425
12.6%
A 46371
9.0%
C 45301
 
8.8%
N 42469
 
8.3%
I 37392
 
7.3%
D 29154
 
5.7%
B 21720
 
4.2%
S 18486
 
3.6%
R 17594
 
3.4%
Other values (15) 115461
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 512899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 74526
14.5%
64425
12.6%
A 46371
9.0%
C 45301
 
8.8%
N 42469
 
8.3%
I 37392
 
7.3%
D 29154
 
5.7%
B 21720
 
4.2%
S 18486
 
3.6%
R 17594
 
3.4%
Other values (15) 115461
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 512899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 74526
14.5%
64425
12.6%
A 46371
9.0%
C 45301
 
8.8%
N 42469
 
8.3%
I 37392
 
7.3%
D 29154
 
5.7%
B 21720
 
4.2%
S 18486
 
3.6%
R 17594
 
3.4%
Other values (15) 115461
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 512899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 74526
14.5%
64425
12.6%
A 46371
9.0%
C 45301
 
8.8%
N 42469
 
8.3%
I 37392
 
7.3%
D 29154
 
5.7%
B 21720
 
4.2%
S 18486
 
3.6%
R 17594
 
3.4%
Other values (15) 115461
22.5%

CODIGO_MONEDA
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size178.2 KiB
1
21907 
54
 
881

Length

Max length2
Median length1
Mean length1.0386607
Min length1

Characters and Unicode

Total characters23669
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 21907
96.1%
54 881
 
3.9%

Length

2025-02-11T10:42:25.752074image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-11T10:42:25.861869image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1 21907
96.1%
54 881
 
3.9%

Most occurring characters

ValueCountFrequency (%)
1 21907
92.6%
5 881
 
3.7%
4 881
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 21907
92.6%
5 881
 
3.7%
4 881
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 21907
92.6%
5 881
 
3.7%
4 881
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 21907
92.6%
5 881
 
3.7%
4 881
 
3.7%

MONTO_APROBADO
Real number (ℝ)

High correlation  Skewed 

Distinct19382
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12640755
Minimum19.54
Maximum3.1544363 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:25.992144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum19.54
5-th percentile780.6435
Q16404.265
median44908.895
Q3217785.95
95-th percentile8000000
Maximum3.1544363 × 1010
Range3.1544363 × 1010
Interquartile range (IQR)211381.69

Descriptive statistics

Standard deviation2.7364312 × 108
Coefficient of variation (CV)21.647688
Kurtosis7960.4082
Mean12640755
Median Absolute Deviation (MAD)43192.105
Skewness76.079521
Sum2.8805752 × 1011
Variance7.4880555 × 1016
MonotonicityNot monotonic
2025-02-11T10:42:26.179023image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25401.6 68
 
0.3%
10000000 51
 
0.2%
38054.24 42
 
0.2%
13680.12 39
 
0.2%
117192 38
 
0.2%
1963.42 35
 
0.2%
500000000 35
 
0.2%
59508 32
 
0.1%
3516.87 31
 
0.1%
2660.94 31
 
0.1%
Other values (19372) 22386
98.2%
ValueCountFrequency (%)
19.54 1
< 0.1%
26.37 1
< 0.1%
38.42 1
< 0.1%
55 2
< 0.1%
56.94 1
< 0.1%
57.19 1
< 0.1%
58.81 1
< 0.1%
58.96 2
< 0.1%
59.05 1
< 0.1%
64.13 1
< 0.1%
ValueCountFrequency (%)
3.154436258 × 10101
 
< 0.1%
8000000000 5
< 0.1%
6659653942 1
 
< 0.1%
6300000000 1
 
< 0.1%
5519565633 2
 
< 0.1%
5000000000 3
< 0.1%
4579393557 1
 
< 0.1%
3539135141 1
 
< 0.1%
3500000000 1
 
< 0.1%
2900000000 1
 
< 0.1%

MONTO_DISPONIBLE
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1006
Distinct (%)4.4%
Missing17
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2407154.8
Minimum0
Maximum7.5 × 109
Zeros21570
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:26.349085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38505.38
Maximum7.5 × 109
Range7.5 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1181821 × 108
Coefficient of variation (CV)46.452438
Kurtosis4437.104
Mean2407154.8
Median Absolute Deviation (MAD)0
Skewness66.256229
Sum5.4813322 × 1010
Variance1.2503312 × 1016
MonotonicityNot monotonic
2025-02-11T10:42:26.491094image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21570
94.7%
59508 16
 
0.1%
190000000 11
 
< 0.1%
15000000 7
 
< 0.1%
5833333.33 7
 
< 0.1%
10000000 6
 
< 0.1%
8333.34 6
 
< 0.1%
2009860.72 5
 
< 0.1%
3000000 5
 
< 0.1%
4198068.96 5
 
< 0.1%
Other values (996) 1133
 
5.0%
(Missing) 17
 
0.1%
ValueCountFrequency (%)
0 21570
94.7%
0.02 1
 
< 0.1%
600 1
 
< 0.1%
834 1
 
< 0.1%
1000 3
 
< 0.1%
2000 2
 
< 0.1%
3000 1
 
< 0.1%
4000 1
 
< 0.1%
5166.66 2
 
< 0.1%
5166.67 2
 
< 0.1%
ValueCountFrequency (%)
7500000000 5
< 0.1%
1000000000 1
 
< 0.1%
537839914.5 1
 
< 0.1%
480004949.1 1
 
< 0.1%
400000000 2
 
< 0.1%
388888888.9 1
 
< 0.1%
305555555.6 1
 
< 0.1%
292455269.8 1
 
< 0.1%
258932411.4 1
 
< 0.1%
250000000 1
 
< 0.1%

MONTO_INICIAL
Real number (ℝ)

High correlation  Skewed 

Distinct19297
Distinct (%)85.4%
Missing204
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean10244431
Minimum19.54
Maximum3.1544363 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:26.749315image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum19.54
5-th percentile771.837
Q16295.4875
median43395.94
Q3209691.21
95-th percentile6600000
Maximum3.1544363 × 1010
Range3.1544363 × 1010
Interquartile range (IQR)203395.72

Descriptive statistics

Standard deviation2.466864 × 108
Coefficient of variation (CV)24.08005
Kurtosis11923.652
Mean10244431
Median Absolute Deviation (MAD)41742.85
Skewness97.260629
Sum2.3136023 × 1011
Variance6.0854181 × 1016
MonotonicityNot monotonic
2025-02-11T10:42:26.909350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25401.6 68
 
0.3%
10000000 48
 
0.2%
38054.24 42
 
0.2%
13680.12 39
 
0.2%
117192 38
 
0.2%
1963.42 35
 
0.2%
500000000 34
 
0.1%
2661.12 31
 
0.1%
2660.94 31
 
0.1%
3516.87 31
 
0.1%
Other values (19287) 22187
97.4%
(Missing) 204
 
0.9%
ValueCountFrequency (%)
19.54 1
< 0.1%
26.37 1
< 0.1%
38.42 1
< 0.1%
55 2
< 0.1%
56.94 1
< 0.1%
57.19 1
< 0.1%
58.81 1
< 0.1%
58.96 2
< 0.1%
59.05 1
< 0.1%
64.13 1
< 0.1%
ValueCountFrequency (%)
3.154436258 × 10101
 
< 0.1%
6659653942 1
 
< 0.1%
6300000000 1
 
< 0.1%
5519565633 2
 
< 0.1%
5000000000 3
< 0.1%
4579393557 1
 
< 0.1%
3539135141 1
 
< 0.1%
3500000000 1
 
< 0.1%
2900000000 1
 
< 0.1%
2500000000 5
< 0.1%

TIPO_TASA
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing204
Missing (%)0.9%
Memory size178.2 KiB
6.0
15976 
0.0
4584 
5.0
1909 
4.0
 
114
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters67752
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row5.0
2nd row6.0
3rd row6.0
4th row6.0
5th row6.0

Common Values

ValueCountFrequency (%)
6.0 15976
70.1%
0.0 4584
 
20.1%
5.0 1909
 
8.4%
4.0 114
 
0.5%
1.0 1
 
< 0.1%
(Missing) 204
 
0.9%

Length

2025-02-11T10:42:27.102173image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-11T10:42:27.248180image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
6.0 15976
70.7%
0.0 4584
 
20.3%
5.0 1909
 
8.5%
4.0 114
 
0.5%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 27168
40.1%
. 22584
33.3%
6 15976
23.6%
5 1909
 
2.8%
4 114
 
0.2%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27168
40.1%
. 22584
33.3%
6 15976
23.6%
5 1909
 
2.8%
4 114
 
0.2%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27168
40.1%
. 22584
33.3%
6 15976
23.6%
5 1909
 
2.8%
4 114
 
0.2%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27168
40.1%
. 22584
33.3%
6 15976
23.6%
5 1909
 
2.8%
4 114
 
0.2%
1 1
 
< 0.1%

TASA_TOTAL
Real number (ℝ)

High correlation 

Distinct1163
Distinct (%)5.1%
Missing204
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean11.652592
Minimum0
Maximum16.95
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:27.398489image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.47
Q111.7276
median11.8132
Q312.06445
95-th percentile13.444
Maximum16.95
Range16.95
Interquartile range (IQR)0.33685

Descriptive statistics

Standard deviation1.5729288
Coefficient of variation (CV)0.13498532
Kurtosis6.8109543
Mean11.652592
Median Absolute Deviation (MAD)0.2068
Skewness-2.2648545
Sum263162.13
Variance2.4741051
MonotonicityNot monotonic
2025-02-11T10:42:27.547619image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.7753 690
 
3.0%
7.47 616
 
2.7%
11.7841 470
 
2.1%
11.7549 391
 
1.7%
11.9105 357
 
1.6%
10.23 356
 
1.6%
11.8083 345
 
1.5%
11.8032 333
 
1.5%
11.8404 312
 
1.4%
12.0664 300
 
1.3%
Other values (1153) 18414
80.8%
ValueCountFrequency (%)
0 4
 
< 0.1%
1.48 2
 
< 0.1%
1.57 2
 
< 0.1%
1.63 1
 
< 0.1%
3.7 1
 
< 0.1%
3.75 11
< 0.1%
4 5
 
< 0.1%
4.3623 1
 
< 0.1%
4.42 17
0.1%
4.422 17
0.1%
ValueCountFrequency (%)
16.95 13
0.1%
15.7205 2
 
< 0.1%
15.63 4
 
< 0.1%
15.6299 5
 
< 0.1%
15.62 5
 
< 0.1%
15.6057 4
 
< 0.1%
15.4337 2
 
< 0.1%
15.4335 8
< 0.1%
15.43 12
0.1%
15.4299 1
 
< 0.1%

PLAZO
Real number (ℝ)

High correlation 

Distinct1214
Distinct (%)5.4%
Missing204
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean652.90166
Minimum30
Maximum12450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:27.738604image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile107
Q1120
median168
Q3455
95-th percentile1822
Maximum12450
Range12420
Interquartile range (IQR)335

Descriptive statistics

Standard deviation1170.7373
Coefficient of variation (CV)1.7931296
Kurtosis17.267487
Mean652.90166
Median Absolute Deviation (MAD)48
Skewness3.7105776
Sum14745131
Variance1370625.8
MonotonicityNot monotonic
2025-02-11T10:42:27.911191image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177 995
 
4.4%
178 865
 
3.8%
119 797
 
3.5%
112 650
 
2.9%
174 640
 
2.8%
179 632
 
2.8%
120 621
 
2.7%
176 576
 
2.5%
114 528
 
2.3%
123 500
 
2.2%
Other values (1204) 15780
69.2%
ValueCountFrequency (%)
30 4
 
< 0.1%
49 5
 
< 0.1%
57 1
 
< 0.1%
77 1
 
< 0.1%
79 1
 
< 0.1%
84 6
 
< 0.1%
87 1
 
< 0.1%
90 1
 
< 0.1%
91 1
 
< 0.1%
94 28
0.1%
ValueCountFrequency (%)
12450 1
 
< 0.1%
12262 2
< 0.1%
9999 1
 
< 0.1%
9987 1
 
< 0.1%
9102 1
 
< 0.1%
9101 1
 
< 0.1%
7951 2
< 0.1%
7950 2
< 0.1%
7922 4
< 0.1%
7897 4
< 0.1%

CODIGO_TIPO_AMORTIZACION
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing204
Missing (%)0.9%
Memory size178.2 KiB
1.0
16572 
5.0
4745 
3.0
 
992
2.0
 
275

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters67752
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 16572
72.7%
5.0 4745
 
20.8%
3.0 992
 
4.4%
2.0 275
 
1.2%
(Missing) 204
 
0.9%

Length

2025-02-11T10:42:28.062111image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-11T10:42:28.186443image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 16572
73.4%
5.0 4745
 
21.0%
3.0 992
 
4.4%
2.0 275
 
1.2%

Most occurring characters

ValueCountFrequency (%)
. 22584
33.3%
0 22584
33.3%
1 16572
24.5%
5 4745
 
7.0%
3 992
 
1.5%
2 275
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 22584
33.3%
0 22584
33.3%
1 16572
24.5%
5 4745
 
7.0%
3 992
 
1.5%
2 275
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 22584
33.3%
0 22584
33.3%
1 16572
24.5%
5 4745
 
7.0%
3 992
 
1.5%
2 275
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 22584
33.3%
0 22584
33.3%
1 16572
24.5%
5 4745
 
7.0%
3 992
 
1.5%
2 275
 
0.4%

AMORTIZACION
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing204
Missing (%)0.9%
Memory size178.2 KiB
CAPITAL E INTERES AL VCTO
16572 
PLAN DE PAGOS
4745 
PAGOS IGUALES CAP. MAS INTS.
 
992
INT. MENSUAL Y CAP. AL VENC.
 
275

Length

Max length28
Median length25
Mean length22.647051
Min length13

Characters and Unicode

Total characters511461
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPLAN DE PAGOS
2nd rowCAPITAL E INTERES AL VCTO
3rd rowCAPITAL E INTERES AL VCTO
4th rowCAPITAL E INTERES AL VCTO
5th rowCAPITAL E INTERES AL VCTO

Common Values

ValueCountFrequency (%)
CAPITAL E INTERES AL VCTO 16572
72.7%
PLAN DE PAGOS 4745
 
20.8%
PAGOS IGUALES CAP. MAS INTS. 992
 
4.4%
INT. MENSUAL Y CAP. AL VENC. 275
 
1.2%
(Missing) 204
 
0.9%

Length

2025-02-11T10:42:28.342203image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-11T10:42:28.493172image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
al 16847
16.2%
capital 16572
16.0%
e 16572
16.0%
interes 16572
16.0%
vcto 16572
16.0%
pagos 5737
 
5.5%
plan 4745
 
4.6%
de 4745
 
4.6%
cap 1267
 
1.2%
iguales 992
 
1.0%
Other values (6) 3084
 
3.0%

Most occurring characters

ValueCountFrequency (%)
81121
15.9%
A 63999
12.5%
E 56003
10.9%
T 50983
10.0%
L 39431
7.7%
I 35403
6.9%
C 34686
6.8%
P 28321
 
5.5%
S 25560
 
5.0%
N 23134
 
4.5%
Other values (9) 72820
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 511461
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
81121
15.9%
A 63999
12.5%
E 56003
10.9%
T 50983
10.0%
L 39431
7.7%
I 35403
6.9%
C 34686
6.8%
P 28321
 
5.5%
S 25560
 
5.0%
N 23134
 
4.5%
Other values (9) 72820
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 511461
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
81121
15.9%
A 63999
12.5%
E 56003
10.9%
T 50983
10.0%
L 39431
7.7%
I 35403
6.9%
C 34686
6.8%
P 28321
 
5.5%
S 25560
 
5.0%
N 23134
 
4.5%
Other values (9) 72820
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 511461
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
81121
15.9%
A 63999
12.5%
E 56003
10.9%
T 50983
10.0%
L 39431
7.7%
I 35403
6.9%
C 34686
6.8%
P 28321
 
5.5%
S 25560
 
5.0%
N 23134
 
4.5%
Other values (9) 72820
14.2%

NUMERO_CUOTAS
Real number (ℝ)

High correlation 

Distinct178
Distinct (%)0.8%
Missing204
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean12.203507
Minimum1
Maximum329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:28.658702image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q312
95-th percentile60
Maximum329
Range328
Interquartile range (IQR)11

Descriptive statistics

Standard deviation24.79449
Coefficient of variation (CV)2.0317512
Kurtosis25.863856
Mean12.203507
Median Absolute Deviation (MAD)0
Skewness4.0389249
Sum275604
Variance614.76671
MonotonicityNot monotonic
2025-02-11T10:42:28.874507image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 16577
72.7%
30 2696
 
11.8%
60 827
 
3.6%
24 188
 
0.8%
36 161
 
0.7%
48 156
 
0.7%
6 133
 
0.6%
84 61
 
0.3%
12 53
 
0.2%
46 52
 
0.2%
Other values (168) 1680
 
7.4%
(Missing) 204
 
0.9%
ValueCountFrequency (%)
1 16577
72.7%
2 1
 
< 0.1%
3 7
 
< 0.1%
4 21
 
0.1%
5 33
 
0.1%
6 133
 
0.6%
7 32
 
0.1%
8 14
 
0.1%
9 12
 
0.1%
10 32
 
0.1%
ValueCountFrequency (%)
329 2
< 0.1%
300 2
< 0.1%
244 4
< 0.1%
240 1
 
< 0.1%
237 1
 
< 0.1%
236 1
 
< 0.1%
234 3
< 0.1%
233 4
< 0.1%
232 4
< 0.1%
231 3
< 0.1%

VALOR_CUOTA
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct20123
Distinct (%)89.2%
Missing234
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean333404.99
Minimum0
Maximum1.6666667 × 109
Zeros299
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:29.048785image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile577.758
Q13417.775
median13277.66
Q367141.32
95-th percentile707683.65
Maximum1.6666667 × 109
Range1.6666667 × 109
Interquartile range (IQR)63723.545

Descriptive statistics

Standard deviation11383127
Coefficient of variation (CV)34.14204
Kurtosis20370.172
Mean333404.99
Median Absolute Deviation (MAD)11897.93
Skewness139.58947
Sum7.5196162 × 109
Variance1.2957557 × 1014
MonotonicityNot monotonic
2025-02-11T10:42:29.227461image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 299
 
1.3%
1000 48
 
0.2%
14191.98 39
 
0.2%
2047.21 35
 
0.2%
2760.12 31
 
0.1%
2760.3 31
 
0.1%
3671.45 31
 
0.1%
2760.69 28
 
0.1%
1874.71 25
 
0.1%
4240.51 23
 
0.1%
Other values (20113) 21964
96.4%
(Missing) 234
 
1.0%
ValueCountFrequency (%)
0 299
1.3%
1 1
 
< 0.1%
20.87 1
 
< 0.1%
27.53 1
 
< 0.1%
30.83 1
 
< 0.1%
39.61 1
 
< 0.1%
44.84 1
 
< 0.1%
58.52 2
 
< 0.1%
59.42 1
 
< 0.1%
60.71 1
 
< 0.1%
ValueCountFrequency (%)
1666666667 1
< 0.1%
186244751.5 1
< 0.1%
186244751.5 1
< 0.1%
94500000 1
< 0.1%
73256193.36 1
< 0.1%
72916666.67 1
< 0.1%
67567567.57 1
< 0.1%
59333286.96 1
< 0.1%
52083333.33 1
< 0.1%
41921318.19 1
< 0.1%

CANTIDAD_CUOTAS_PAGADAS
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct117
Distinct (%)0.5%
Missing257
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean4.623674
Minimum0
Maximum211
Zeros16848
Zeros (%)73.9%
Negative0
Negative (%)0.0%
Memory size178.2 KiB
2025-02-11T10:42:29.399883image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile25
Maximum211
Range211
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.080761
Coefficient of variation (CV)2.6128055
Kurtosis65.303448
Mean4.623674
Median Absolute Deviation (MAD)0
Skewness6.1029592
Sum104176
Variance145.94479
MonotonicityNot monotonic
2025-02-11T10:42:29.549973image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16848
73.9%
1 289
 
1.3%
2 228
 
1.0%
22 220
 
1.0%
16 219
 
1.0%
12 216
 
0.9%
3 215
 
0.9%
20 209
 
0.9%
15 203
 
0.9%
14 200
 
0.9%
Other values (107) 3684
 
16.2%
(Missing) 257
 
1.1%
ValueCountFrequency (%)
0 16848
73.9%
1 289
 
1.3%
2 228
 
1.0%
3 215
 
0.9%
4 191
 
0.8%
5 144
 
0.6%
6 144
 
0.6%
7 122
 
0.5%
8 148
 
0.6%
9 184
 
0.8%
ValueCountFrequency (%)
211 1
 
< 0.1%
208 1
 
< 0.1%
191 1
 
< 0.1%
190 3
< 0.1%
188 1
 
< 0.1%
183 4
< 0.1%
181 1
 
< 0.1%
175 1
 
< 0.1%
174 4
< 0.1%
168 1
 
< 0.1%

Interactions

2025-02-11T10:42:08.393166image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:23.788306image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:26.237788image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:28.692533image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:31.168576image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:33.732901image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:36.039795image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:38.709615image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:41.216789image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:43.704800image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:46.244767image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:48.840261image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:51.351066image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:53.783417image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:56.368337image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:58.771644image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:01.254216image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:03.651075image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:06.109983image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:08.508871image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:23.936098image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:26.374812image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:28.832221image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:31.289436image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:33.848138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:36.174389image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:38.822548image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:41.351834image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:43.951017image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:46.372788image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:48.952210image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:51.484087image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:53.894683image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:56.524898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:58.900730image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:01.389841image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:03.765551image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:06.213385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:08.616827image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:24.057215image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:26.490501image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:28.978115image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:31.416623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:33.984932image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:36.299400image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:38.950470image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:41.473181image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:44.050957image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:46.521477image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:49.092812image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:51.619231image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:53.996125image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:56.653008image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:59.004598image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:01.536354image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:03.894329image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:06.318917image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:08.733685image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:24.207066image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:26.604281image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:29.094483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:31.553736image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:34.124492image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:36.445186image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:39.079756image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:41.608514image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:44.167406image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:46.681862image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:49.215724image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:51.786229image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:54.136206image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:56.769266image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:59.119888image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:01.696194image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:04.023811image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:06.448281image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:08.866598image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:24.333483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:26.721826image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:29.213120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:31.671349image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:34.271881image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:36.568584image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:39.232321image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:41.733648image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:44.287233image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:46.838838image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:49.353068image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:51.920526image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:54.264010image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:56.897057image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:59.273465image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:01.839501image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:04.155644image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:06.578435image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:08.993185image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:24.462795image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:26.844265image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:29.339021image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:31.804836image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:34.386474image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:36.701998image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:39.364215image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:41.861574image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:44.411416image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:46.970523image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:49.503842image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:52.082783image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:54.395400image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:57.026426image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:59.385574image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:01.954827image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:04.270870image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:06.716747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:09.134340image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:24.606344image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:26.985762image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:29.503247image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:31.932279image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:34.533354image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:36.821236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:39.478452image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:42.000338image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:44.587136image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:47.154350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:49.655807image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:52.195249image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:54.541100image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:57.160528image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:59.520270image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:02.088284image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:04.421660image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:06.853623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:09.255519image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:24.739547image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:27.104286image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:29.626755image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:32.043826image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:34.630152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:36.959892image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2025-02-11T10:41:42.142985image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2025-02-11T10:41:27.323909image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:29.855504image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:32.283385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:34.842169image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:37.231999image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:39.920546image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:42.392934image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:44.948207image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:47.532660image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:50.099681image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:52.557636image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:54.946232image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:57.514971image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:59.910292image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:02.480466image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:04.776292image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:07.203546image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:09.601809image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:25.099203image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:27.428590image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:29.964955image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:32.414187image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:34.939411image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:37.375380image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:40.061204image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:42.507741image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:45.080621image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:47.687069image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:50.235390image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:52.687219image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:55.173978image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:57.629599image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:00.008350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:02.607713image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:04.888804image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:07.345743image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:09.729275image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:25.219019image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:27.547983image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:30.095092image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:32.561898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:35.040327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:37.509577image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:40.210924image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:42.667771image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:45.201735image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:47.806270image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:50.365616image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:52.814511image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:55.305434image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:57.761175image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:00.131629image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:02.715158image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:05.020050image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:07.460357image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:09.877774image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:25.344831image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:27.684696image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:30.231557image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:32.705068image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:35.163145image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:37.646293image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:40.358889image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:42.822986image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:45.340248image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:47.938866image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:50.478969image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:52.915451image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:55.417984image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:57.892821image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:00.348081image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:02.816433image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:05.145831image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:07.584310image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:09.997561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:25.473249image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:27.822339image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:30.357955image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:32.841463image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:35.275317image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:37.809855image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:40.484508image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:42.974334image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:45.470212image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:48.053134image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:50.593183image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:53.049783image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:55.539763image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:58.020520image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:00.488350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:02.940199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:05.273930image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:07.714717image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:10.124422image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:25.623297image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:27.955808image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:30.481787image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:32.984317image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:35.411843image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:37.932872image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:40.607152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:43.107640image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:45.617816image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:48.192656image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:50.723022image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:53.172338image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:55.668428image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:58.126589image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:00.615482image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:03.063536image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:05.508463image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:07.839463image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:10.271622image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:25.731283image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:28.066199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:30.633932image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:33.191527image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:35.533762image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:38.187363image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:40.747147image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:43.226394image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:45.725458image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:48.358913image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:50.844692image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:53.286013image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:55.835441image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:58.257951image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:00.738698image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:03.192841image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:05.626454image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:07.974327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:10.405601image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:25.863178image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:28.185595image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:30.761417image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:33.326252image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:35.673384image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:38.304283image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:40.851491image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:43.341752image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:45.860285image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:48.499011image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:50.976564image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:53.406832image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:55.956338image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:58.373188image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:00.858435image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:03.304219image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:05.743198image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:08.077942image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:10.517193image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:25.999571image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:28.443137image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:30.897328image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:33.462387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:35.805929image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:38.447337image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:40.947512image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:43.458151image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:45.988636image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:48.614697image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:51.100898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:53.535905image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:56.111387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:58.499845image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:00.973704image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:03.412482image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:05.864175image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:08.181283image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:10.648231image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:26.120902image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:28.583514image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:31.019634image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:33.625123image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:35.924611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:38.578901image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:41.112854image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:43.571937image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:46.122692image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:48.738241image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:51.246342image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:53.666156image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:56.258803image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:41:58.642730image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:01.111119image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:03.555811image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:06.000695image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-11T10:42:08.286220image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2025-02-11T10:42:29.686935image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
AMORTIZACIONAÑO_REGISTROAÑO_VENCIMIENTOCANTIDAD_CUOTAS_PAGADASCODIGO_CLIENTECODIGO_MONEDACODIGO_TIPO_AMORTIZACIONCODIGO_TIPO_IDENTIFICACIONDELEGACION_MUNICIPIODESC_LINEA_FINANCIERADIA_REGISTRODIA_VENCIMIENTOENTIDAD_FEDERATIVAMES_REGISTROMES_VENCIMIENTOMONTO_APROBADOMONTO_DISPONIBLEMONTO_INICIALNACIONALIDADNO_EMPLEADOSNUMERO_CONTRATONUMERO_CUOTASNUMERO_PRESTAMOPAISPLAZOPRODUCTOSEXOTASA_TOTALTIPO_SECTORTIPO_SUBSECTORTIPO_TASAVALOR_CUOTAVENTAS_NETAS
AMORTIZACION1.0000.4980.0460.3050.2860.1641.0000.3500.6900.5860.0930.4040.6190.4100.4390.0560.0370.0580.0190.0810.4060.3740.4070.0190.5380.7200.0750.3410.4290.6360.6200.0110.055
AÑO_REGISTRO0.4981.000-0.712-0.893-0.2540.5450.4980.3330.7510.408-0.026-0.2570.6840.296-0.483-0.482-0.205-0.4710.0000.3910.700-0.8290.6920.000-0.6560.4560.0710.1580.2900.3760.4800.1220.284
AÑO_VENCIMIENTO0.046-0.7121.0000.8340.3190.0000.0460.0051.0000.1450.0240.2631.000-0.3980.4630.5350.2270.5320.000-0.385-0.6140.900-0.6170.0000.7040.3580.0000.0380.0000.0970.074-0.125-0.282
CANTIDAD_CUOTAS_PAGADAS0.305-0.8930.8341.0000.2670.2760.3050.0860.6270.3130.0330.2720.495-0.4130.5220.5800.3440.5800.000-0.396-0.7390.959-0.7390.0000.7370.3430.000-0.0470.1260.2540.382-0.107-0.286
CODIGO_CLIENTE0.286-0.2540.3190.2671.0000.2810.2860.3710.9470.2230.0200.1950.8030.0180.0430.093-0.0810.0970.000-0.503-0.0680.278-0.0720.0000.0470.2550.205-0.0820.1810.3550.194-0.134-0.396
CODIGO_MONEDA0.1640.5450.0000.2760.2811.0000.1640.1321.0000.4570.0650.2181.0000.1210.2610.0000.0000.0000.0000.2060.5180.4890.5350.0000.5850.9990.0000.8030.2580.3040.2310.0000.067
CODIGO_TIPO_AMORTIZACION1.0000.4980.0460.3050.2860.1641.0000.3500.6900.5860.0930.4040.6190.4100.4390.0560.0370.0580.0190.0810.4060.3740.4070.0190.5380.7200.0750.3410.4290.6360.6200.0110.055
CODIGO_TIPO_IDENTIFICACION0.3500.3330.0050.0860.3710.1320.3501.0000.9700.3520.1080.3380.8930.2210.1940.0130.0050.0090.0150.0320.3220.1060.3350.0150.3860.5401.0000.1600.2770.5890.3990.0000.012
DELEGACION_MUNICIPIO0.6900.7511.0000.6270.9471.0000.6900.9701.0000.8640.2380.5220.9880.6050.5481.0001.0001.0001.0001.0000.7710.7780.7711.0000.8090.8160.9750.8520.8720.9210.9051.0001.000
DESC_LINEA_FINANCIERA0.5860.4080.1450.3130.2230.4570.5860.3520.8641.0000.0900.2610.7590.2530.2810.1100.0000.1330.0840.3360.3750.4130.3780.0840.4210.7600.1860.2900.3140.4820.6850.0690.471
DIA_REGISTRO0.093-0.0260.0240.0330.0200.0650.0930.1080.2380.0901.0000.1870.210-0.255-0.0680.0650.0230.0670.0210.054-0.0900.032-0.0950.0210.0460.0830.1140.0130.0850.1470.0970.0590.073
DIA_VENCIMIENTO0.404-0.2570.2630.2720.1950.2180.4040.3380.5220.2610.1871.0000.463-0.192-0.0920.1950.0080.1980.000-0.067-0.2350.274-0.2420.0000.2400.2880.1140.1640.1920.2890.343-0.027-0.094
ENTIDAD_FEDERATIVA0.6190.6841.0000.4950.8031.0000.6190.8930.9880.7590.2100.4631.0000.4780.4721.0001.0001.0001.0001.0000.7450.6780.7451.0000.7270.7060.7860.8230.7520.6980.8581.0001.000
MES_REGISTRO0.4100.296-0.398-0.4130.0180.1210.4100.2210.6050.253-0.255-0.1920.4781.000-0.050-0.282-0.157-0.2790.024-0.0400.723-0.4020.7260.024-0.6220.2570.052-0.2300.1980.2850.3140.0200.043
MES_VENCIMIENTO0.439-0.4830.4630.5220.0430.2610.4390.1940.5480.281-0.068-0.0920.472-0.0501.0000.3790.2320.3720.026-0.221-0.3440.548-0.3430.0260.6030.2980.066-0.0870.1790.2510.353-0.022-0.145
MONTO_APROBADO0.056-0.4820.5350.5800.0930.0000.0560.0131.0000.1100.0650.1951.000-0.2820.3791.0000.3420.9990.000-0.086-0.4440.619-0.4370.0000.4950.3691.0000.0950.0710.1400.0490.6600.011
MONTO_DISPONIBLE0.037-0.2050.2270.344-0.0810.0000.0370.0051.0000.0000.0230.0081.000-0.1570.2320.3421.0000.3200.0000.110-0.2180.357-0.2070.0000.2280.0261.0000.2640.0340.0850.0470.1830.177
MONTO_INICIAL0.058-0.4710.5320.5800.0970.0000.0580.0091.0000.1330.0670.1981.000-0.2790.3720.9990.3201.0000.000-0.095-0.4370.618-0.4370.0000.4950.3801.0000.0950.0670.1370.0400.6610.000
NACIONALIDAD0.0190.0000.0000.0000.0000.0000.0190.0151.0000.0840.0210.0001.0000.0240.0260.0000.0000.0001.0000.0000.0000.0300.0001.0000.0540.1110.0030.0130.0140.1850.0650.0000.000
NO_EMPLEADOS0.0810.391-0.385-0.396-0.5030.2060.0810.0321.0000.3360.054-0.0671.000-0.040-0.221-0.0860.110-0.0950.0001.0000.112-0.3810.1200.000-0.1340.3481.0000.1690.1960.3110.0730.2470.674
NUMERO_CONTRATO0.4060.700-0.614-0.739-0.0680.5180.4060.3220.7710.375-0.090-0.2350.7450.723-0.344-0.444-0.218-0.4370.0000.1121.000-0.6961.0000.000-0.8550.4210.048-0.1320.2760.3520.4340.0660.140
NUMERO_CUOTAS0.374-0.8290.9000.9590.2780.4890.3740.1060.7780.4130.0320.2740.678-0.4020.5480.6190.3570.6180.030-0.381-0.6961.000-0.6960.0300.7580.5360.000-0.0100.2020.3280.430-0.091-0.269
NUMERO_PRESTAMO0.4070.692-0.617-0.739-0.0720.5350.4070.3350.7710.378-0.095-0.2420.7450.726-0.343-0.437-0.207-0.4370.0000.1201.000-0.6961.0000.000-0.8550.4280.047-0.1320.2700.3530.4340.0660.152
PAIS0.0190.0000.0000.0000.0000.0000.0190.0151.0000.0840.0210.0001.0000.0240.0260.0000.0000.0001.0000.0000.0000.0300.0001.0000.0540.1110.0030.0130.0140.1850.0650.0000.000
PLAZO0.538-0.6560.7040.7370.0470.5850.5380.3860.8090.4210.0460.2400.727-0.6220.6030.4950.2280.4950.054-0.134-0.8550.758-0.8550.0541.0000.5610.0740.1030.2650.3990.472-0.057-0.155
PRODUCTO0.7200.4560.3580.3430.2550.9990.7200.5400.8160.7600.0830.2880.7060.2570.2980.3690.0260.3800.1110.3480.4210.5360.4280.1110.5611.0000.0800.4510.4480.3710.8080.4390.425
SEXO0.0750.0710.0000.0000.2050.0000.0751.0000.9750.1860.1140.1140.7860.0520.0661.0001.0001.0000.0031.0000.0480.0000.0470.0030.0740.0801.0000.0260.2300.3080.0801.0001.000
TASA_TOTAL0.3410.1580.038-0.047-0.0820.8030.3410.1600.8520.2900.0130.1640.823-0.230-0.0870.0950.2640.0950.0130.169-0.132-0.010-0.1320.0130.1030.4510.0261.0000.2230.3130.3160.0970.083
TIPO_SECTOR0.4290.2900.0000.1260.1810.2580.4290.2770.8720.3140.0850.1920.7520.1980.1790.0710.0340.0670.0140.1960.2760.2020.2700.0140.2650.4480.2300.2231.0001.0000.4770.1190.242
TIPO_SUBSECTOR0.6360.3760.0970.2540.3550.3040.6360.5890.9210.4820.1470.2890.6980.2850.2510.1400.0850.1370.1850.3110.3520.3280.3530.1850.3990.3710.3080.3131.0001.0000.7200.1150.385
TIPO_TASA0.6200.4800.0740.3820.1940.2310.6200.3990.9050.6850.0970.3430.8580.3140.3530.0490.0470.0400.0650.0730.4340.4300.4340.0650.4720.8080.0800.3160.4770.7201.0000.0230.036
VALOR_CUOTA0.0110.122-0.125-0.107-0.1340.0000.0110.0001.0000.0690.059-0.0271.0000.020-0.0220.6600.1830.6610.0000.2470.066-0.0910.0660.000-0.0570.4391.0000.0970.1190.1150.0231.0000.272
VENTAS_NETAS0.0550.284-0.282-0.286-0.3960.0670.0550.0121.0000.4710.073-0.0941.0000.043-0.1450.0110.1770.0000.0000.6740.140-0.2690.1520.000-0.1550.4251.0000.0830.2420.3850.0360.2721.000

Missing values

2025-02-11T10:42:10.981746image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-11T10:42:11.673033image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-11T10:42:12.253600image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CODIGO_CLIENTECODIGO_TIPO_IDENTIFICACIONNOMBRESPRIMER_APELLIDOSEGUNDO_APELLIDOSEXOFECHA_DE_NACIMIENTORAZON_SOCIALNOMBRE_COMERCIALNO_EMPLEADOSVENTAS_NETASPAISNACIONALIDADDELEGACION_MUNICIPIOENTIDAD_FEDERATIVATIPO_SECTORTIPO_SUBSECTORTIPO_RAMATIPO_CLASENUMERO_CONTRATONUMERO_PRESTAMOFECHA_APERTURAAÑO_REGISTROMES_REGISTRODIA_REGISTROFECHA_VENCIMIENTOAÑO_VENCIMIENTOMES_VENCIMIENTODIA_VENCIMIENTOPRODUCTODESC_LINEA_FINANCIERACODIGO_MONEDAMONTO_APROBADOMONTO_DISPONIBLEMONTO_INICIALTIPO_TASATASA_TOTALPLAZOCODIGO_TIPO_AMORTIZACIONAMORTIZACIONNUMERO_CUOTASVALOR_CUOTACANTIDAD_CUOTAS_PAGADAS
0630417NaNNaNNaNNaNNaNLAMINA DESPLEGADA SA DE CVLAMINA DESPLEGADA SA DE CV7671.104424e+06MEXICOMEXICANASANTA CATARINA LA FAMANUEVO LEONINDUSTRIAS MANUFACTURERASINDUSTRIAS METALICAS BASICASINDUSTRIAS BASICAS DE METALES NO FERROSOS. (I) EL TRATAMIENTFUNDICION, LAMINACION, EXTRUSION, REFINACION Y/O ESTIRAJE DE39154419405327964.02023-11-14 00:00:00202311142025-04-16 00:00:0020254162DO. PISO, M.N. SIMPLE2DO PISO NO BANC C/AVAL BANC11381152.290.01381152.295.011.3234519.05.0PLAN DE PAGOS17.084079.4711.0
1990037NaNNaNNaNNaNNaNCAFE EL MARINOCAFE EL MARINO2494.900000e+07MEXICOMEXICANAMAZATLANSINALOAINDUSTRIAS MANUFACTURERASPRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACOBENEFICIO Y MOLIENDA DE CEREALES Y OTROS PRODUCTOS AGRICOLASBENEFICIO DE CAFE40689525420677745.02024-11-01 00:00:0020241112025-04-03 00:00:002025432DO. PISO, M.N. HABILITACION O AVIO2DO. PISO, SIN AVAL BANCARIO115921.290.015921.296.012.7549153.01.0CAPITAL E INTERES AL VCTO1.016784.210.0
2990037NaNNaNNaNNaNNaNCAFE EL MARINOCAFE EL MARINO2494.900000e+07MEXICOMEXICANAMAZATLANSINALOAINDUSTRIAS MANUFACTURERASPRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACOBENEFICIO Y MOLIENDA DE CEREALES Y OTROS PRODUCTOS AGRICOLASBENEFICIO DE CAFE40585141419633900.02024-10-08 00:00:0020241082025-03-03 00:00:002025332DO. PISO, M.N. HABILITACION O AVIO2DO. PISO, SIN AVAL BANCARIO15886.970.05886.976.012.6743146.01.0CAPITAL E INTERES AL VCTO1.06189.190.0
3990037NaNNaNNaNNaNNaNCAFE EL MARINOCAFE EL MARINO2494.900000e+07MEXICOMEXICANAMAZATLANSINALOAINDUSTRIAS MANUFACTURERASPRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACOBENEFICIO Y MOLIENDA DE CEREALES Y OTROS PRODUCTOS AGRICOLASBENEFICIO DE CAFE40585144419633937.02024-10-08 00:00:0020241082025-02-17 00:00:0020252172DO. PISO, M.N. HABILITACION O AVIO2DO. PISO, SIN AVAL BANCARIO15930.550.05930.556.012.6743132.01.0CAPITAL E INTERES AL VCTO1.06206.430.0
41231737NaNNaNNaNNaNNaNSCHETTINO HERMANOS, S. DE R.L. DE C.V.SCHETTINO HERMANOS, S. DE R.L. DE C.V.2812.106982e+09MEXICOMEXICANAORIZABAVERACRUZCOMERCIOCOMERCIO AL POR MAYORCOMERCIO DE PRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACO AL PORCHILE SECO Y ESPECIAS (I) PASTA PARA MOLE Y SIMILARES40373372417516219.02024-08-19 00:00:0020248192025-02-11 00:00:0020252112DO. PISO, M.N. HABILITACION O AVIO2DO. PISO, BANCARIO193107.000.093107.006.012.1024176.01.0CAPITAL E INTERES AL VCTO1.098615.800.0
51231737NaNNaNNaNNaNNaNSCHETTINO HERMANOS, S. DE R.L. DE C.V.SCHETTINO HERMANOS, S. DE R.L. DE C.V.2812.106982e+09MEXICOMEXICANAORIZABAVERACRUZCOMERCIOCOMERCIO AL POR MAYORCOMERCIO DE PRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACO AL PORCHILE SECO Y ESPECIAS (I) PASTA PARA MOLE Y SIMILARES40618611419968586.02024-10-16 00:00:00202410162025-04-07 00:00:002025472DO. PISO, M.N. HABILITACION O AVIO2DO. PISO, BANCARIO1258057.030.0258057.036.011.6871173.01.0CAPITAL E INTERES AL VCTO1.0272550.970.0
61281967NaNNaNNaNNaNNaNALIMENTOS COMPEAN S.A. DE CVALIMENTOS COMPEAN S.A. DE CV503.000000e+07MEXICOMEXICANASAN LUIS POTOSISAN LUIS POTOSIINDUSTRIAS MANUFACTURERASPRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACOELABORACION DE OTROS PRODUCTOS ALIMENTICIOS PARA EL CONSUMOELABORACION DE OTROS PRODUCTOS ALIMENTICIOS PARA CONSUMO HUM40482018418602669.02024-09-12 00:00:0020249122025-02-27 00:00:0020252272DO. PISO, M.N. HABILITACION O AVIO2DO PISO NO BANC C/AVAL BANC141021.740.041021.746.012.1526168.01.0CAPITAL E INTERES AL VCTO1.043348.540.0
71281967NaNNaNNaNNaNNaNALIMENTOS COMPEAN S.A. DE CVALIMENTOS COMPEAN S.A. DE CV503.000000e+07MEXICOMEXICANASAN LUIS POTOSISAN LUIS POTOSIINDUSTRIAS MANUFACTURERASPRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACOELABORACION DE OTROS PRODUCTOS ALIMENTICIOS PARA EL CONSUMOELABORACION DE OTROS PRODUCTOS ALIMENTICIOS PARA CONSUMO HUM40482019418602650.02024-09-12 00:00:0020249122025-02-27 00:00:0020252272DO. PISO, M.N. HABILITACION O AVIO2DO PISO NO BANC C/AVAL BANC180915.740.080915.746.012.1526168.01.0CAPITAL E INTERES AL VCTO1.085503.820.0
81281967NaNNaNNaNNaNNaNALIMENTOS COMPEAN S.A. DE CVALIMENTOS COMPEAN S.A. DE CV503.000000e+07MEXICOMEXICANASAN LUIS POTOSISAN LUIS POTOSIINDUSTRIAS MANUFACTURERASPRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACOELABORACION DE OTROS PRODUCTOS ALIMENTICIOS PARA EL CONSUMOELABORACION DE OTROS PRODUCTOS ALIMENTICIOS PARA CONSUMO HUM40482015418602641.02024-09-12 00:00:0020249122025-02-27 00:00:0020252272DO. PISO, M.N. HABILITACION O AVIO2DO PISO NO BANC C/AVAL BANC141021.740.041021.746.012.1526168.01.0CAPITAL E INTERES AL VCTO1.043348.540.0
91281967NaNNaNNaNNaNNaNALIMENTOS COMPEAN S.A. DE CVALIMENTOS COMPEAN S.A. DE CV503.000000e+07MEXICOMEXICANASAN LUIS POTOSISAN LUIS POTOSIINDUSTRIAS MANUFACTURERASPRODUCTOS ALIMENTICIOS, BEBIDAS Y TABACOELABORACION DE OTROS PRODUCTOS ALIMENTICIOS PARA EL CONSUMOELABORACION DE OTROS PRODUCTOS ALIMENTICIOS PARA CONSUMO HUM40482014418602632.02024-09-12 00:00:0020249122025-02-27 00:00:0020252272DO. PISO, M.N. HABILITACION O AVIO2DO PISO NO BANC C/AVAL BANC123043.600.023043.606.012.1526168.01.0CAPITAL E INTERES AL VCTO1.024350.640.0
CODIGO_CLIENTECODIGO_TIPO_IDENTIFICACIONNOMBRESPRIMER_APELLIDOSEGUNDO_APELLIDOSEXOFECHA_DE_NACIMIENTORAZON_SOCIALNOMBRE_COMERCIALNO_EMPLEADOSVENTAS_NETASPAISNACIONALIDADDELEGACION_MUNICIPIOENTIDAD_FEDERATIVATIPO_SECTORTIPO_SUBSECTORTIPO_RAMATIPO_CLASENUMERO_CONTRATONUMERO_PRESTAMOFECHA_APERTURAAÑO_REGISTROMES_REGISTRODIA_REGISTROFECHA_VENCIMIENTOAÑO_VENCIMIENTOMES_VENCIMIENTODIA_VENCIMIENTOPRODUCTODESC_LINEA_FINANCIERACODIGO_MONEDAMONTO_APROBADOMONTO_DISPONIBLEMONTO_INICIALTIPO_TASATASA_TOTALPLAZOCODIGO_TIPO_AMORTIZACIONAMORTIZACIONNUMERO_CUOTASVALOR_CUOTACANTIDAD_CUOTAS_PAGADAS
22778289507635RAULALEGRIARIOSM1978-11-12 00:00:00NaNNaN15001.0MEXICOMEXICANANaNNaNINDUSTRIAS MANUFACTURERASPRODUCTOS METALICOS ,MAQUINARIA Y EQUIPO.(I) INSTRUMENTOS QFABRICACION, REPARACION Y/O ENSAMBLE DE MAQUINARIA Y EQUIPOFABRICACION DE EQUIPOS Y APARATOS DE AIRE ACONDICIONADO, REF40704517420827672.02024-11-05 00:00:0020241152029-09-29 00:00:002029929CREDITO SIMPLE FIDEDIRECTO CON DOCUMENTO178109.990.078109.990.011.66001788.05.0PLAN DE PAGOS30.01643.300.0
22779289507815AURORAGONGORARODRIGUEZF1990-10-01 00:00:00NaNNaN15001.0MEXICOMEXICANANaNNaNINDUSTRIAS MANUFACTURERASPRODUCTOS METALICOS ,MAQUINARIA Y EQUIPO.(I) INSTRUMENTOS QFABRICACION, REPARACION Y/O ENSAMBLE DE MAQUINARIA Y EQUIPOFABRICACION DE EQUIPOS Y APARATOS DE AIRE ACONDICIONADO, REF40704525420827752.02024-11-05 00:00:0020241152029-09-29 00:00:002029929CREDITO SIMPLE FIDEDIRECTO CON DOCUMENTO1180450.000.0180450.000.011.66001788.05.0PLAN DE PAGOS30.03795.520.0
22780289507905MARIO ALBERTOCASTILLONRIOSM1966-07-17 00:00:00NaNNaN15001.0MEXICOMEXICANANaNNaNINDUSTRIAS MANUFACTURERASPRODUCTOS METALICOS ,MAQUINARIA Y EQUIPO.(I) INSTRUMENTOS QFABRICACION, REPARACION Y/O ENSAMBLE DE MAQUINARIA Y EQUIPOFABRICACION DE EQUIPOS Y APARATOS DE AIRE ACONDICIONADO, REF40704518420827681.02024-11-05 00:00:0020241152029-09-29 00:00:002029929CREDITO SIMPLE FIDEDIRECTO CON DOCUMENTO1131116.260.0131116.260.011.66001788.05.0PLAN DE PAGOS30.02757.880.0
22781289508525FIDELJOSEJULIOM1980-08-27 00:00:00NaNNaN10800000.0MEXICOMEXICANANaNNaNCONSTRUCCIONCONSTRUCCIONCONSTRUCCION DE OBRAS DE URBANIZACIONCONSTRUCCION DE OBRAS DE URBANIZACION40681011420592612.02024-10-30 00:00:00202410302025-02-19 00:00:0020252192DO. PISO, M.N. HABILITACION O AVIO2DO. PISO, BANCARIO11865171.290.01865171.296.011.7440112.01.0CAPITAL E INTERES AL VCTO1.01933318.810.0
22782289510115MARIORODRIGUEZCAMBRONM1968-07-17 00:00:00NaNNaN15001.0MEXICOMEXICANANaNNaNINDUSTRIAS MANUFACTURERASPRODUCTOS METALICOS ,MAQUINARIA Y EQUIPO.(I) INSTRUMENTOS QFABRICACION, REPARACION Y/O ENSAMBLE DE MAQUINARIA Y EQUIPOFABRICACION DE EQUIPOS Y APARATOS DE AIRE ACONDICIONADO, REF40704519420827690.02024-11-05 00:00:0020241152029-09-29 00:00:002029929CREDITO SIMPLE FIDEDIRECTO CON DOCUMENTO1119700.660.0119700.660.011.66001788.05.0PLAN DE PAGOS30.02517.970.0
22783289512805BRENDAVAZQUEZESPINOSAF1995-09-19 00:00:00NaNNaN15001.0MEXICOMEXICANANaNNaNINDUSTRIAS MANUFACTURERASPRODUCTOS METALICOS ,MAQUINARIA Y EQUIPO.(I) INSTRUMENTOS QFABRICACION, REPARACION Y/O ENSAMBLE DE MAQUINARIA Y EQUIPOFABRICACION DE EQUIPOS Y APARATOS DE AIRE ACONDICIONADO, REF40704520420827707.02024-11-05 00:00:0020241152029-09-29 00:00:002029929CREDITO SIMPLE FIDEDIRECTO CON DOCUMENTO155740.360.055740.360.011.66001788.05.0PLAN DE PAGOS30.01172.650.0
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